Distance_from_boundary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e621921-a910-4361-9a2a-c451f84211cd",
   "metadata": {},
   "source": [
    "## For a given \"lake\" boundary contour, \n",
    "### we will want to composite gridded quantities by distance from that boundary contour. \n",
    "## Do it with `groupby` operation on a distance field\n",
    "##### Computed and written out \n",
    "\n",
    "Shapely [object.buffer](https://shapely.readthedocs.io/en/stable/manual.html#spatial-analysis-methods) measures these buffer distances, negative values being outside the shape and positive values inside.  \n",
    "\n",
    "[rioxarray](https://corteva.github.io/rioxarray/stable/examples/examples.html) can clip data to a polynomial or buffer \n",
    "\n",
    "Shapely/geopandas has a `distance` method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "31300c9e-fd42-4360-87e4-e108dc43f480",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import geopandas as gp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53036a8b-6644-4f4d-9fed-7ee8550b4517",
   "metadata": {},
   "source": [
    "## grab a case in 2014-2018 \n",
    "#### lakes dataframe was \"improved\" in LakeCaseStudy.ipynb\n",
    "by adding more columns: firstt, dur_days, yyyy, mm, coastlat for screening purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1ec77ac-3dd7-4f5b-8d12-0094f0c43289",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('ccvls_stats_2014-2018.improved.csv')\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd27b987-4769-471f-bd21-225888ca2013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0.1                                853\n",
       "Unnamed: 0                                  863\n",
       "lasttime                    2017-05-28 11:00:00\n",
       "duration                        7 days 04:00:00\n",
       "areatime                            5690.601728\n",
       "tqv_values                                 55.0\n",
       "maxarea                               50.852737\n",
       "filename        2017_05_28_11_lat5p291S.geojson\n",
       "ymdh                              2017_05_28_11\n",
       "firsttime                   2017-05-21 07:00:00\n",
       "dur_days                                      7\n",
       "yyyy                                       2017\n",
       "mm                                            5\n",
       "coastlat                                 -5.291\n",
       "Name: 853, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equatorcases = df[ abs(df.coastlat) < 10 ] # 162 of them exceeding 1 day \n",
    "eq7cases = equatorcases[ equatorcases.dur_days > 6 ]\n",
    "\n",
    "# Pick a clean one: all polygon, no holes (multipolygons)\n",
    "case = eq7cases.iloc[5]\n",
    "case\n",
    "# showme(case.filename) see LakeCaseStudy.ipynb for showme function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "d1137f58-1177-4ccc-aa6f-3e19497b184d",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = case.filename\n",
    "gdf = gp.read_file('GEOJSONS/'+filename)\n",
    "gdf\n",
    "# to avoid area warnings, use an equal-area map projection (CRS)\n",
    "# https://gis.stackexchange.com/questions/372564/userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas\n",
    "gdf_area = gdf.set_crs(epsg = \"4326\", inplace = True)         #.to_crs('+proj=cea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "3000a46c-aec0-4cf7-84ce-fdd00bc91b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot(column='time', facecolor='none', cmap='jet') #, alpha=1) #, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "a7719af9-af93-420f-9f2b-7d43fc070e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v9/8pr92c8s0m14426rc7jfm2c80000gn/T/ipykernel_96932/634526862.py:11: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  gdf_area.centroid.to_crs(gdf.crs).plot(ax=ax,color='black')\n",
      "/Users/brianmapes/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/geopandas/plotting.py:51: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.\n",
      "  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'polygons w/no holes? lasting 7 days, one merger')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with map context and centroids \n",
    "fig, ax = plt.subplots(figsize=[8,7])\n",
    "ax.set_aspect('equal')\n",
    "ax.set_xlim(35, 95)\n",
    "ax.set_ylim(-30,30)\n",
    "\n",
    "gdf.plot(ax=ax, column='time', facecolor='none', \\\n",
    "                cmap='jet', alpha=1) #, legend=True)\n",
    "\n",
    "# Plot centroids \n",
    "gdf_area.centroid.to_crs(gdf.crs).plot(ax=ax,color='black')\n",
    "\n",
    "#gdf.boundary.plot(ax=ax, color='black')\n",
    "path = gp.datasets.get_path('naturalearth_lowres') # Geography background\n",
    "mapdf = gp.read_file(path)\n",
    "mapdf.boundary.plot(ax=ax)\n",
    "    \n",
    "ax.set_title('polygons w/no holes? lasting 7 days, one merger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "3c5efacb-6f4f-4299-be14-32b6690f9b72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v9/8pr92c8s0m14426rc7jfm2c80000gn/T/ipykernel_96932/3246302327.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  plt.plot(gdf.time, gdf_area.area)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17a5ccca0>]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(gdf.time, gdf_area.area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "e24ea23f-3e6f-4760-adae-cc35141bcf29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v9/8pr92c8s0m14426rc7jfm2c80000gn/T/ipykernel_96932/2264692458.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  gdf.iloc[ gdf_area.area.argmax() ].geometry\n"
     ]
    },
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"55.46311542078111 -11.214463838746754 13.355316824175787 7.912666838722904\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,-14.516260838770604)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.2671063364835157\" opacity=\"0.6\" d=\"M 62.5,-10.719822474888392 L 63.125,-10.655664780560661 L 63.25222602257362,-10.5 L 63.59531626981847,-10.0 L 63.75,-9.883127848307293 L 64.16049412318638,-9.5 L 64.375,-9.310331645764801 L 64.58863410396852,-9.0 L 65.0,-8.627747582607583 L 65.14430745442708,-8.5 L 65.625,-8.019307454427084 L 65.65841674804688,-8.0 L 66.25,-7.648430524553572 L 66.41371013737526,-7.5 L 66.875,-7.02779790406586 L 66.8945848869555,-7.0 L 67.17955907185873,-6.5 L 67.5,-6.161332156680047 L 67.69114353345788,-6.0 L 68.125,-5.640407252956081 L 68.32379088109853,-5.5 L 68.125,-5.221692766462054 L 68.01415361676898,-5.0 L 67.5,-4.557036884014423 L 67.37127092149522,-4.5 L 66.875,-4.340306116881983 L 66.25,-4.099412378771552 L 66.0247688293457,-4.0 L 65.625,-3.922936129282756 L 65.0,-3.959846310499238 L 64.375,-3.796438363882212 L 63.75,-3.828510199652778 L 63.175815400623144,-4.0 L 63.125,-4.017511768830128 L 62.5,-4.369355758818069 L 62.28297584935238,-4.5 L 61.875,-4.69328518161526 L 61.25,-4.900312792702657 L 60.625,-4.945393078432882 L 60.0,-4.703587431066177 L 59.375,-4.662962911023172 L 58.75,-4.645337632123161 L 58.3565701041252,-4.5 L 58.125,-4.445736557690066 L 57.5,-4.285840163584879 L 56.875,-4.329351806640625 L 56.42806425548735,-4.5 L 56.25,-4.666193305121528 L 55.95775678463947,-5.0 L 55.996226844283456,-5.5 L 56.163051898662864,-6.0 L 56.25,-6.162929775478604 L 56.49614996659129,-6.5 L 56.875,-6.881358974027318 L 56.96079882625419,-7.0 L 57.411006047175476,-7.5 L 57.5,-7.571776347257654 L 57.907277930597346,-8.0 L 58.125,-8.179760272686298 L 58.496706826346255,-8.5 L 58.75,-8.684014067778715 L 59.2154251390202,-9.0 L 59.375,-9.115187370914152 L 60.0,-9.441329328981164 L 60.10580432273654,-9.5 L 60.625,-9.772241463932968 L 61.0052543032953,-10.0 L 61.25,-10.145463252128858 L 61.875,-10.476641296709657 L 61.93308940686678,-10.5 L 62.5,-10.719822474888392 z\" /></g></svg>"
      ],
      "text/plain": [
       "<POLYGON ((62.5 -10.72, 63.125 -10.656, 63.252 -10.5, 63.595 -10, 63.75 -9.8...>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of largest blob at any time \n",
    "\n",
    "gdf.iloc[ gdf_area.area.argmax() ].geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98848709-ba57-4dfe-ab67-d3f8515a5fcf",
   "metadata": {},
   "source": [
    "# Learn about boundary, buffer, centroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "301b626b-9e05-4a05-80fa-b8fe95ee8024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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     "execution_count": 11,
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   "source": [
    "# 50th polygon is distinctive shape \n",
    "blob = gdf.iloc[ 50 ]\n",
    "blob.geometry.boundary"
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  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5642c7ca-20f3-4ba4-9bee-418012949b7b",
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     "execution_count": 48,
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   ],
   "source": [
    "# Buffer is the region just inside the boundary \n",
    "blobuf = blob.geometry.boundary.buffer(.7, single_sided=True) # units are degrees\n",
    "blobuf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d5d600db-9d0a-4f77-ac5d-333730b87857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42.25679859114371, -4.090041136659944)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# We will want to further subdivide by angle from a centroid to compute propagation direction \n",
    "centroidlon = blob.geometry.centroid.bounds[0]\n",
    "centroidlat = blob.geometry.centroid.bounds[1]\n",
    "\n",
    "centroidlon, centroidlat"
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  {
   "cell_type": "markdown",
   "id": "a509081c-e275-478d-a490-b4e9105da977",
   "metadata": {},
   "source": [
    "# Try rioxarray `clip` method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "303e0707-2763-4229-93b1-687c94dbff9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import rioxarray as rxr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "6195548d-377b-45c9-a6a0-1c5308108308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianmapes/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/xarray/coding/times.py:144: SerializationWarning: Ambiguous reference date string: 1-1-1 00:00:0.0. The first value is assumed to be the year hence will be padded with zeros to remove the ambiguity (the padded reference date string is: 0001-1-1 00:00:0.0). To remove this message, remove the ambiguity by padding your reference date strings with zeros.\n",
      "  warnings.warn(warning_msg, SerializationWarning)\n"
     ]
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       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (time: 172, lat: 121, lon: 97)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2017-05-21T08:00:00.000003 ... 2017-05...\n",
       "  * lat          (lat) float64 -30.0 -29.5 -29.0 -28.5 ... 28.5 29.0 29.5 30.0\n",
       "  * lon          (lon) float64 35.0 35.62 36.25 36.88 ... 93.12 93.75 94.38 95.0\n",
       "    spatial_ref  int64 0\n",
       "Data variables: (12/24)\n",
       "    disph        (time, lat, lon) float32 ...\n",
       "    ps           (time, lat, lon) float32 ...\n",
       "    qv10m        (time, lat, lon) float32 ...\n",
       "    qv2m         (time, lat, lon) float32 ...\n",
       "    slp          (time, lat, lon) float32 ...\n",
       "    t10m         (time, lat, lon) float32 ...\n",
       "    ...           ...\n",
       "    u10m         (time, lat, lon) float32 ...\n",
       "    u2m          (time, lat, lon) float32 ...\n",
       "    u50m         (time, lat, lon) float32 ...\n",
       "    v10m         (time, lat, lon) float32 ...\n",
       "    v2m          (time, lat, lon) float32 ...\n",
       "    v50m         (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:        MERRA-2 inst1_2d_asm_Nx: 2d,1-Hourly,Instantaneous,Single-L...\n",
       "    Conventions:  COARDS\\nGrADS\n",
       "    dataType:     Grid\n",
       "    history:      Thu Jan 19 02:15:29 GMT 2023 : imported by GrADS Data Serve...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fb4c3570-786e-45fa-913e-fe0c9078cdd2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb4c3570-786e-45fa-913e-fe0c9078cdd2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 172</li><li><span class='xr-has-index'>lat</span>: 121</li><li><span class='xr-has-index'>lon</span>: 97</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2fa7c76b-6334-445e-aa61-5ef691b12292' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2fa7c76b-6334-445e-aa61-5ef691b12292' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-05-21T08:00:00.000003 ... 2...</div><input id='attrs-87546352-3402-456d-bb62-57b57da1cc72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87546352-3402-456d-bb62-57b57da1cc72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-891fa254-86e0-42c4-afda-e5d8dac0f344' class='xr-var-data-in' type='checkbox'><label for='data-891fa254-86e0-42c4-afda-e5d8dac0f344' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>376200</dd><dt><span>grads_min :</span></dt><dd>00z01jan1980</dd><dt><span>grads_step :</span></dt><dd>60mn</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>00z01jan1980</dd><dt><span>maximum :</span></dt><dd>23z30nov2022</dd><dt><span>resolution :</span></dt><dd>0.041666668</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-05-21T08:00:00.000003000&#x27;, &#x27;2017-05-21T09:00:00.000000000&#x27;,\n",
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       "       &#x27;2017-05-28T08:00:00.000003000&#x27;, &#x27;2017-05-28T09:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-28T09:59:59.999997000&#x27;, &#x27;2017-05-28T11:00:00.000003000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-30.0 -29.5 -29.0 ... 29.5 30.0</div><input id='attrs-50743dfc-00e2-4536-aaff-70dfe4384a3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50743dfc-00e2-4536-aaff-70dfe4384a3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9676c782-2aa8-4c44-98c5-d4dfe42b5800' class='xr-var-data-in' type='checkbox'><label for='data-9676c782-2aa8-4c44-98c5-d4dfe42b5800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>361</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-90.0</dd><dt><span>maximum :</span></dt><dd>90.0</dd><dt><span>resolution :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-30. , -29.5, -29. , -28.5, -28. , -27.5, -27. , -26.5, -26. , -25.5,\n",
       "       -25. , -24.5, -24. , -23.5, -23. , -22.5, -22. , -21.5, -21. , -20.5,\n",
       "       -20. , -19.5, -19. , -18.5, -18. , -17.5, -17. , -16.5, -16. , -15.5,\n",
       "       -15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ,  15.5,  16. ,  16.5,  17. ,  17.5,  18. ,  18.5,  19. ,  19.5,\n",
       "        20. ,  20.5,  21. ,  21.5,  22. ,  22.5,  23. ,  23.5,  24. ,  24.5,\n",
       "        25. ,  25.5,  26. ,  26.5,  27. ,  27.5,  28. ,  28.5,  29. ,  29.5,\n",
       "        30. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.0 35.62 36.25 ... 94.38 95.0</div><input id='attrs-dce1746f-7c1f-4279-9958-eff28ce25927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dce1746f-7c1f-4279-9958-eff28ce25927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-331aa208-5dfd-46d9-bc71-94487c8ade4c' class='xr-var-data-in' type='checkbox'><label for='data-331aa208-5dfd-46d9-bc71-94487c8ade4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>576</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>-180.0</dd><dt><span>maximum :</span></dt><dd>179.375</dd><dt><span>resolution :</span></dt><dd>0.625</dd></dl></div><div class='xr-var-data'><pre>array([35.   , 35.625, 36.25 , 36.875, 37.5  , 38.125, 38.75 , 39.375, 40.   ,\n",
       "       40.625, 41.25 , 41.875, 42.5  , 43.125, 43.75 , 44.375, 45.   , 45.625,\n",
       "       46.25 , 46.875, 47.5  , 48.125, 48.75 , 49.375, 50.   , 50.625, 51.25 ,\n",
       "       51.875, 52.5  , 53.125, 53.75 , 54.375, 55.   , 55.625, 56.25 , 56.875,\n",
       "       57.5  , 58.125, 58.75 , 59.375, 60.   , 60.625, 61.25 , 61.875, 62.5  ,\n",
       "       63.125, 63.75 , 64.375, 65.   , 65.625, 66.25 , 66.875, 67.5  , 68.125,\n",
       "       68.75 , 69.375, 70.   , 70.625, 71.25 , 71.875, 72.5  , 73.125, 73.75 ,\n",
       "       74.375, 75.   , 75.625, 76.25 , 76.875, 77.5  , 78.125, 78.75 , 79.375,\n",
       "       80.   , 80.625, 81.25 , 81.875, 82.5  , 83.125, 83.75 , 84.375, 85.   ,\n",
       "       85.625, 86.25 , 86.875, 87.5  , 88.125, 88.75 , 89.375, 90.   , 90.625,\n",
       "       91.25 , 91.875, 92.5  , 93.125, 93.75 , 94.375, 95.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-7f4bb61b-c866-4856-a060-2c52c80aefd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f4bb61b-c866-4856-a060-2c52c80aefd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72d0a07c-7f72-4cd5-a80b-29574e24b6af' class='xr-var-data-in' type='checkbox'><label for='data-72d0a07c-7f72-4cd5-a80b-29574e24b6af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-82d5aec1-c883-48e2-b492-6c0a422eb4a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-82d5aec1-c883-48e2-b492-6c0a422eb4a1' class='xr-section-summary' >Data variables: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>disph</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b65cdac-2263-4f10-b53e-8397e65b58a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b65cdac-2263-4f10-b53e-8397e65b58a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d3fefc7-98be-4b3e-ae8f-1654e1f16052' class='xr-var-data-in' type='checkbox'><label for='data-7d3fefc7-98be-4b3e-ae8f-1654e1f16052' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>zero plane displacement height m </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ps</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c373c239-c0eb-4c58-97c0-780486edf790' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c373c239-c0eb-4c58-97c0-780486edf790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0f8603e-bd68-4065-955b-55febdeb1cf5' class='xr-var-data-in' type='checkbox'><label for='data-f0f8603e-bd68-4065-955b-55febdeb1cf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>surface pressure pa </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qv10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-010c8786-ec63-42ae-902f-7800dd50cc5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-010c8786-ec63-42ae-902f-7800dd50cc5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4621552b-21f2-466a-9230-5d2d175fc42e' class='xr-var-data-in' type='checkbox'><label for='data-4621552b-21f2-466a-9230-5d2d175fc42e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>10-meter specific humidity kg kg-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qv2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0942f75-d60d-433a-bbf8-af4a636e2993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0942f75-d60d-433a-bbf8-af4a636e2993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26a2650c-7aa0-4bde-b68a-c0572d375367' class='xr-var-data-in' type='checkbox'><label for='data-26a2650c-7aa0-4bde-b68a-c0572d375367' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>2-meter specific humidity kg kg-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slp</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b6bf14b-e77f-4352-8c57-85cfca2747aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b6bf14b-e77f-4352-8c57-85cfca2747aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cf1db5a-09a1-4659-a8d4-2c898d5fe89b' class='xr-var-data-in' type='checkbox'><label for='data-2cf1db5a-09a1-4659-a8d4-2c898d5fe89b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>sea level pressure pa </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94e04504-a75e-4d7a-bb65-ea9dad24b4ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94e04504-a75e-4d7a-bb65-ea9dad24b4ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcfd19d4-15e8-4f2e-b702-f843715a0230' class='xr-var-data-in' type='checkbox'><label for='data-bcfd19d4-15e8-4f2e-b702-f843715a0230' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>10-meter air temperature k </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7c17d35-d0bb-4031-a077-11775bd0b9c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7c17d35-d0bb-4031-a077-11775bd0b9c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8124c78e-c2e0-4c2b-b8e4-4418f90ccb45' class='xr-var-data-in' type='checkbox'><label for='data-8124c78e-c2e0-4c2b-b8e4-4418f90ccb45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>2-meter air temperature k </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>to3</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dcaeea38-f471-494b-9519-c9e3ac6d1be8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcaeea38-f471-494b-9519-c9e3ac6d1be8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65289582-4636-4b27-a88f-fc7e90fcbe11' class='xr-var-data-in' type='checkbox'><label for='data-65289582-4636-4b27-a88f-fc7e90fcbe11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dobsons</dd><dt><span>long_name :</span></dt><dd>total column ozone dobsons </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tox</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ec77dbba-e488-4b20-a767-3d8954d7ffe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec77dbba-e488-4b20-a767-3d8954d7ffe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2680222b-b307-444e-9d47-58c43d46e8fc' class='xr-var-data-in' type='checkbox'><label for='data-2680222b-b307-444e-9d47-58c43d46e8fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total column odd oxygen kg m-2 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0797dc2a-4cfa-449e-a711-6ac94a67ea6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0797dc2a-4cfa-449e-a711-6ac94a67ea6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a380fc99-ac29-4f30-849c-e29aa97ee144' class='xr-var-data-in' type='checkbox'><label for='data-a380fc99-ac29-4f30-849c-e29aa97ee144' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total precipitable ice water kg m-2 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tql</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d7c02b2d-5f57-400b-9f17-c7b8423ab681' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7c02b2d-5f57-400b-9f17-c7b8423ab681' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed629ef8-fb82-4c6f-9a0e-322d75949c99' class='xr-var-data-in' type='checkbox'><label for='data-ed629ef8-fb82-4c6f-9a0e-322d75949c99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total precipitable liquid water kg m-2 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf4c0ef7-f222-41a0-8702-240763f5ffc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf4c0ef7-f222-41a0-8702-240763f5ffc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ce2a7c1-86e7-4f28-80d7-850a24de2e08' class='xr-var-data-in' type='checkbox'><label for='data-4ce2a7c1-86e7-4f28-80d7-850a24de2e08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total precipitable water vapor kg m-2 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppb</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3494539b-b43c-4a8d-9d4e-07ab65f67814' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3494539b-b43c-4a8d-9d4e-07ab65f67814' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4f03cd6-7947-4ad9-9276-b6d524ee13ef' class='xr-var-data-in' type='checkbox'><label for='data-b4f03cd6-7947-4ad9-9276-b6d524ee13ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>tropopause pressure based on blended estimate pa </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppt</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49c8bc00-5f8b-4eb1-8e68-9b3c2ef1a9d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49c8bc00-5f8b-4eb1-8e68-9b3c2ef1a9d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef5a215d-a9f1-4675-809b-f98f09e6b1e7' class='xr-var-data-in' type='checkbox'><label for='data-ef5a215d-a9f1-4675-809b-f98f09e6b1e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>tropopause pressure based on thermal estimate pa </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppv</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cea4c0c5-e809-47f2-8d22-b8d29f930a0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cea4c0c5-e809-47f2-8d22-b8d29f930a0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09a2400c-3fe8-42fd-82b9-2a3e0de7682c' class='xr-var-data-in' type='checkbox'><label for='data-09a2400c-3fe8-42fd-82b9-2a3e0de7682c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>tropopause pressure based on epv estimate pa </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tropq</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-951865b9-e67c-4255-9bb9-078bf7201754' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-951865b9-e67c-4255-9bb9-078bf7201754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7fbeb0d-8609-4754-b24e-57666bc59119' class='xr-var-data-in' type='checkbox'><label for='data-a7fbeb0d-8609-4754-b24e-57666bc59119' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>tropopause specific humidity using blended tropp estimate kg kg-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tropt</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef1e658e-2826-4363-8141-123c9963e3b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef1e658e-2826-4363-8141-123c9963e3b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9439076d-3baa-4290-b03c-ec91fa171cf7' class='xr-var-data-in' type='checkbox'><label for='data-9439076d-3baa-4290-b03c-ec91fa171cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>tropopause temperature using blended tropp estimate k </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41654913-10f0-40e9-8c23-335e5467c52f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41654913-10f0-40e9-8c23-335e5467c52f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ee8b9f-9db0-44e5-804c-ee7f113a2e35' class='xr-var-data-in' type='checkbox'><label for='data-88ee8b9f-9db0-44e5-804c-ee7f113a2e35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>surface skin temperature k </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0541c00-63b0-4809-9377-ca1cb4dbeeaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0541c00-63b0-4809-9377-ca1cb4dbeeaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a787aa7-6b39-4a3a-ad6f-72cd31476038' class='xr-var-data-in' type='checkbox'><label for='data-1a787aa7-6b39-4a3a-ad6f-72cd31476038' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>10-meter eastward wind m s-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c02fdd35-4614-4e98-8b57-6492d2218405' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c02fdd35-4614-4e98-8b57-6492d2218405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c48ef49-f840-40d6-b1e2-e542af72900e' class='xr-var-data-in' type='checkbox'><label for='data-8c48ef49-f840-40d6-b1e2-e542af72900e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>2-meter eastward wind m s-1n </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u50m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7270b3f-50ee-43c6-ab10-51bb24f64db0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7270b3f-50ee-43c6-ab10-51bb24f64db0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bf7d90e-df8b-4e2e-adfc-ffb89e6efb19' class='xr-var-data-in' type='checkbox'><label for='data-0bf7d90e-df8b-4e2e-adfc-ffb89e6efb19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>eastward wind at 50 meters m s-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5a42031-2c09-4e88-9592-669bd853daab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5a42031-2c09-4e88-9592-669bd853daab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1962b90-0f5c-4347-9af4-e30f07d56930' class='xr-var-data-in' type='checkbox'><label for='data-e1962b90-0f5c-4347-9af4-e30f07d56930' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>10-meter northward wind m s-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-91f24bf9-d782-4d26-b4be-a7f70af03d70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91f24bf9-d782-4d26-b4be-a7f70af03d70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d40f0fb-da47-4be1-82db-18d1fd9649a3' class='xr-var-data-in' type='checkbox'><label for='data-4d40f0fb-da47-4be1-82db-18d1fd9649a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>2-meter northward wind m s-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v50m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4840be8f-48f0-430a-bb81-0cf9bbbb523f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4840be8f-48f0-430a-bb81-0cf9bbbb523f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a57255c-4aed-4f77-9fd8-aa8ca16537c3' class='xr-var-data-in' type='checkbox'><label for='data-0a57255c-4aed-4f77-9fd8-aa8ca16537c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>northward wind at 50 meters m s-1 </dd></dl></div><div class='xr-var-data'><pre>[2018764 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a308b93-4583-415c-87e4-af33ae8f970f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a308b93-4583-415c-87e4-af33ae8f970f' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>MERRA-2 inst1_2d_asm_Nx: 2d,1-Hourly,Instantaneous,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4</dd><dt><span>Conventions :</span></dt><dd>COARDS\n",
       "GrADS</dd><dt><span>dataType :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Thu Jan 19 02:15:29 GMT 2023 : imported by GrADS Data Server 2.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 172, lat: 121, lon: 97)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2017-05-21T08:00:00.000003 ... 2017-05...\n",
       "  * lat          (lat) float64 -30.0 -29.5 -29.0 -28.5 ... 28.5 29.0 29.5 30.0\n",
       "  * lon          (lon) float64 35.0 35.62 36.25 36.88 ... 93.12 93.75 94.38 95.0\n",
       "    spatial_ref  int64 0\n",
       "Data variables: (12/24)\n",
       "    disph        (time, lat, lon) float32 ...\n",
       "    ps           (time, lat, lon) float32 ...\n",
       "    qv10m        (time, lat, lon) float32 ...\n",
       "    qv2m         (time, lat, lon) float32 ...\n",
       "    slp          (time, lat, lon) float32 ...\n",
       "    t10m         (time, lat, lon) float32 ...\n",
       "    ...           ...\n",
       "    u10m         (time, lat, lon) float32 ...\n",
       "    u2m          (time, lat, lon) float32 ...\n",
       "    u50m         (time, lat, lon) float32 ...\n",
       "    v10m         (time, lat, lon) float32 ...\n",
       "    v2m          (time, lat, lon) float32 ...\n",
       "    v50m         (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:        MERRA-2 inst1_2d_asm_Nx: 2d,1-Hourly,Instantaneous,Single-L...\n",
       "    Conventions:  COARDS\\nGrADS\n",
       "    dataType:     Grid\n",
       "    history:      Thu Jan 19 02:15:29 GMT 2023 : imported by GrADS Data Serve..."
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MERRA: get oneself authenticated first \n",
    "#testURL = 'https://goldsmr5.gesdisc.eosdis.nasa.gov/dods/M2I3NPASM'\n",
    "#testURL = 'https://rda.ucar.edu/thredds/dodsC/files/g/ds084.1/2021/20210719/gfs.0p25.2021071900.f024.grib2'\n",
    "\n",
    "# 2D fields vertical integrals \n",
    "URL = 'dods://goldsmr4.gesdisc.eosdis.nasa.gov/dods/M2I1NXASM'\n",
    "ds = xr.open_dataset(URL, decode_coords='all').sel(time=slice(case.firsttime,case.lasttime), \\\n",
    "                                                   lat=slice(-30,30),lon=slice(35,95))\n",
    "\n",
    "# Convert to rioxarray dataset, with x,y set to lon,lat and a standard CRS defined\n",
    "dsr = ds.rio.set_spatial_dims('lon','lat',inplace=True)\n",
    "dsr.rio.write_crs(\"epsg:4326\", inplace=True)\n",
    "dsr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "615c12bb-a348-4f1f-b27d-7e267b67a14c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2017-05-27T06:00:00'"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fe209e3f-5ace-4f41-9d91-be7972400e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (time: 1, lat: 101, lon: 113)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2017-05-27T06:00:00\n",
       "  * lat          (lat) float64 -25.0 -24.5 -24.0 -23.5 ... 23.5 24.0 24.5 25.0\n",
       "  * lon          (lon) float64 30.0 30.62 31.25 31.88 ... 98.75 99.38 100.0\n",
       "    spatial_ref  int64 0\n",
       "Data variables: (12/24)\n",
       "    disph        (time, lat, lon) float32 0.3308 9.457 7.119 ... 2.465 4.119\n",
       "    ps           (time, lat, lon) float32 8.702e+04 8.712e+04 ... 8.073e+04\n",
       "    qv10m        (time, lat, lon) float32 0.005655 0.005672 ... 0.01127 0.01074\n",
       "    qv2m         (time, lat, lon) float32 0.007309 0.007129 ... 0.01175 0.01109\n",
       "    slp          (time, lat, lon) float32 1.013e+05 1.013e+05 ... 1.005e+05\n",
       "    t10m         (time, lat, lon) float32 287.6 287.4 289.6 ... 295.4 295.6\n",
       "    ...           ...\n",
       "    u10m         (time, lat, lon) float32 0.2923 1.059 1.395 ... -1.349 -0.4553\n",
       "    u2m          (time, lat, lon) float32 0.1214 0.4446 ... -0.9255 -0.2712\n",
       "    u50m         (time, lat, lon) float32 0.4691 1.549 1.77 ... -1.591 -0.5641\n",
       "    v10m         (time, lat, lon) float32 1.388 1.26 2.481 ... 1.823 1.704 1.633\n",
       "    v2m          (time, lat, lon) float32 0.633 0.5338 1.653 ... 1.141 1.013\n",
       "    v50m         (time, lat, lon) float32 2.207 2.043 3.103 ... 2.032 1.994\n",
       "Attributes:\n",
       "    title:        MERRA-2 inst1_2d_asm_Nx: 2d,1-Hourly,Instantaneous,Single-L...\n",
       "    Conventions:  COARDS\\nGrADS\n",
       "    dataType:     Grid\n",
       "    history:      Thu Jan 19 02:15:29 GMT 2023 : imported by GrADS Data Serve...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e4f00e46-0a0a-4d29-8ca6-55ab632f974d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4f00e46-0a0a-4d29-8ca6-55ab632f974d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lat</span>: 101</li><li><span class='xr-has-index'>lon</span>: 113</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-766ee7f0-3072-4d2d-b8fb-5e25d2d4b934' class='xr-section-summary-in' type='checkbox'  checked><label for='section-766ee7f0-3072-4d2d-b8fb-5e25d2d4b934' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-05-27T06:00:00</div><input id='attrs-e47a195d-1351-4f55-8766-21e7e4dc2c92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e47a195d-1351-4f55-8766-21e7e4dc2c92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc0a7fce-e743-446d-a3db-0423b08ecc1b' class='xr-var-data-in' type='checkbox'><label for='data-fc0a7fce-e743-446d-a3db-0423b08ecc1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>376200</dd><dt><span>grads_min :</span></dt><dd>00z01jan1980</dd><dt><span>grads_step :</span></dt><dd>60mn</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>00z01jan1980</dd><dt><span>maximum :</span></dt><dd>23z30nov2022</dd><dt><span>resolution :</span></dt><dd>0.041666668</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-05-27T06:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-25.0 -24.5 -24.0 ... 24.5 25.0</div><input id='attrs-b0226187-d5e1-46ed-8334-cd1688b3457c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0226187-d5e1-46ed-8334-cd1688b3457c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-203498d7-973b-49e8-a638-d50d91e75a77' class='xr-var-data-in' type='checkbox'><label for='data-203498d7-973b-49e8-a638-d50d91e75a77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>361</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-90.0</dd><dt><span>maximum :</span></dt><dd>90.0</dd><dt><span>resolution :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-25. , -24.5, -24. , -23.5, -23. , -22.5, -22. , -21.5, -21. , -20.5,\n",
       "       -20. , -19.5, -19. , -18.5, -18. , -17.5, -17. , -16.5, -16. , -15.5,\n",
       "       -15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ,  15.5,  16. ,  16.5,  17. ,  17.5,  18. ,  18.5,  19. ,  19.5,\n",
       "        20. ,  20.5,  21. ,  21.5,  22. ,  22.5,  23. ,  23.5,  24. ,  24.5,\n",
       "        25. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.0 30.62 31.25 ... 99.38 100.0</div><input id='attrs-ec385327-d5f8-4f15-90d2-0a67082e6449' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec385327-d5f8-4f15-90d2-0a67082e6449' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95e7f82-a01e-4047-8b52-27b73ddc9cb5' class='xr-var-data-in' type='checkbox'><label for='data-b95e7f82-a01e-4047-8b52-27b73ddc9cb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>576</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>-180.0</dd><dt><span>maximum :</span></dt><dd>179.375</dd><dt><span>resolution :</span></dt><dd>0.625</dd></dl></div><div class='xr-var-data'><pre>array([ 30.   ,  30.625,  31.25 ,  31.875,  32.5  ,  33.125,  33.75 ,  34.375,\n",
       "        35.   ,  35.625,  36.25 ,  36.875,  37.5  ,  38.125,  38.75 ,  39.375,\n",
       "        40.   ,  40.625,  41.25 ,  41.875,  42.5  ,  43.125,  43.75 ,  44.375,\n",
       "        45.   ,  45.625,  46.25 ,  46.875,  47.5  ,  48.125,  48.75 ,  49.375,\n",
       "        50.   ,  50.625,  51.25 ,  51.875,  52.5  ,  53.125,  53.75 ,  54.375,\n",
       "        55.   ,  55.625,  56.25 ,  56.875,  57.5  ,  58.125,  58.75 ,  59.375,\n",
       "        60.   ,  60.625,  61.25 ,  61.875,  62.5  ,  63.125,  63.75 ,  64.375,\n",
       "        65.   ,  65.625,  66.25 ,  66.875,  67.5  ,  68.125,  68.75 ,  69.375,\n",
       "        70.   ,  70.625,  71.25 ,  71.875,  72.5  ,  73.125,  73.75 ,  74.375,\n",
       "        75.   ,  75.625,  76.25 ,  76.875,  77.5  ,  78.125,  78.75 ,  79.375,\n",
       "        80.   ,  80.625,  81.25 ,  81.875,  82.5  ,  83.125,  83.75 ,  84.375,\n",
       "        85.   ,  85.625,  86.25 ,  86.875,  87.5  ,  88.125,  88.75 ,  89.375,\n",
       "        90.   ,  90.625,  91.25 ,  91.875,  92.5  ,  93.125,  93.75 ,  94.375,\n",
       "        95.   ,  95.625,  96.25 ,  96.875,  97.5  ,  98.125,  98.75 ,  99.375,\n",
       "       100.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-a6c0aa4e-ce69-44c5-9896-77d138d48ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6c0aa4e-ce69-44c5-9896-77d138d48ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-701ef9ac-c83c-4ad1-82fb-25ce214543a6' class='xr-var-data-in' type='checkbox'><label for='data-701ef9ac-c83c-4ad1-82fb-25ce214543a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c676486-b7f4-4652-9747-127182676395' class='xr-section-summary-in' type='checkbox'  ><label for='section-6c676486-b7f4-4652-9747-127182676395' class='xr-section-summary' >Data variables: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>disph</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.3308 9.457 7.119 ... 2.465 4.119</div><input id='attrs-923a2245-4375-49cf-9c85-0997d1352a44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-923a2245-4375-49cf-9c85-0997d1352a44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f528376d-9a8d-4e74-883f-ad07acddacf5' class='xr-var-data-in' type='checkbox'><label for='data-f528376d-9a8d-4e74-883f-ad07acddacf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>zero plane displacement height m </dd></dl></div><div class='xr-var-data'><pre>array([[[0.330811, 9.457031, ..., 0.      , 0.      ],\n",
       "        [0.310303, 0.715332, ..., 0.      , 0.      ],\n",
       "        ...,\n",
       "        [0.054718, 0.054535, ..., 4.068359, 5.041016],\n",
       "        [0.054733, 0.054703, ..., 2.464844, 4.119141]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ps</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>8.702e+04 8.712e+04 ... 8.073e+04</div><input id='attrs-3ea262c8-a1a9-4556-ac3a-ad17d3673f1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ea262c8-a1a9-4556-ac3a-ad17d3673f1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e26e4bea-d951-4c6e-b6bc-cedea6a1edd2' class='xr-var-data-in' type='checkbox'><label for='data-e26e4bea-d951-4c6e-b6bc-cedea6a1edd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>surface pressure pa </dd></dl></div><div class='xr-var-data'><pre>array([[[ 87019.09,  87115.09, ..., 102335.09, 102319.09],\n",
       "        [ 89523.09,  91543.09, ..., 102295.09, 102271.09],\n",
       "        ...,\n",
       "        [ 98639.09,  99527.09, ...,  83761.09,  82191.09],\n",
       "        [ 98767.09,  99695.09, ...,  81360.09,  80730.09]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qv10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.005655 0.005672 ... 0.01074</div><input id='attrs-f180181d-70df-40d9-af6a-36b2bcd1c03e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f180181d-70df-40d9-af6a-36b2bcd1c03e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b963d25-fed8-4dce-8119-a067bd968cb4' class='xr-var-data-in' type='checkbox'><label for='data-9b963d25-fed8-4dce-8119-a067bd968cb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>10-meter specific humidity kg kg-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[0.005655, 0.005672, ..., 0.011339, 0.011579],\n",
       "        [0.00567 , 0.005502, ..., 0.011514, 0.011747],\n",
       "        ...,\n",
       "        [0.004112, 0.004127, ..., 0.011919, 0.01116 ],\n",
       "        [0.004116, 0.004133, ..., 0.011266, 0.010736]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qv2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.007309 0.007129 ... 0.01109</div><input id='attrs-e0c23e27-1240-46cf-8c41-c2f83f3f247e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0c23e27-1240-46cf-8c41-c2f83f3f247e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10e31409-65d9-4a26-ae8a-1fbc7593ecdb' class='xr-var-data-in' type='checkbox'><label for='data-10e31409-65d9-4a26-ae8a-1fbc7593ecdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>2-meter specific humidity kg kg-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[0.007309, 0.007129, ..., 0.01185 , 0.012071],\n",
       "        [0.006139, 0.006197, ..., 0.012029, 0.012254],\n",
       "        ...,\n",
       "        [0.004112, 0.004127, ..., 0.012487, 0.011598],\n",
       "        [0.004118, 0.004133, ..., 0.011751, 0.011095]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slp</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.013e+05 1.013e+05 ... 1.005e+05</div><input id='attrs-ad60074c-aa5e-46c5-bbac-0628bb28569f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad60074c-aa5e-46c5-bbac-0628bb28569f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8571462-c270-481f-84fc-5c65149b5f91' class='xr-var-data-in' type='checkbox'><label for='data-e8571462-c270-481f-84fc-5c65149b5f91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>sea level pressure pa </dd></dl></div><div class='xr-var-data'><pre>array([[[101268.91, 101274.91, ..., 102334.91, 102321.91],\n",
       "        [101370.91, 101368.91, ..., 102291.91, 102272.91],\n",
       "        ...,\n",
       "        [101258.91, 101231.91, ..., 100501.41, 100519.91],\n",
       "        [101251.91, 101229.91, ..., 100515.41, 100478.41]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>287.6 287.4 289.6 ... 295.4 295.6</div><input id='attrs-76d694ae-03fa-41d3-9700-b26e299fda3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76d694ae-03fa-41d3-9700-b26e299fda3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b34ff5b-9662-45e4-ab0b-7db85fcf9dc3' class='xr-var-data-in' type='checkbox'><label for='data-4b34ff5b-9662-45e4-ab0b-7db85fcf9dc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>10-meter air temperature k </dd></dl></div><div class='xr-var-data'><pre>array([[[287.64096, 287.4066 , ..., 293.57065, 293.68002],\n",
       "        [286.31284, 288.86752, ..., 293.70346, 293.88315],\n",
       "        ...,\n",
       "        [300.7269 , 302.74252, ..., 298.21127, 297.1644 ],\n",
       "        [301.07065, 302.69565, ..., 295.4144 , 295.55502]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>286.5 285.8 289.0 ... 296.4 296.7</div><input id='attrs-62ef6e76-6fc8-4d5a-aae5-90ac3ad017b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62ef6e76-6fc8-4d5a-aae5-90ac3ad017b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59fe5370-eda0-4dae-a421-74d6907a219f' class='xr-var-data-in' type='checkbox'><label for='data-59fe5370-eda0-4dae-a421-74d6907a219f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>2-meter air temperature k </dd></dl></div><div class='xr-var-data'><pre>array([[[286.5078 , 285.8125 , ..., 293.73438, 293.83594],\n",
       "        [286.1953 , 288.57812, ..., 293.875  , 294.0547 ],\n",
       "        ...,\n",
       "        [301.32812, 303.28125, ..., 299.46875, 298.48438],\n",
       "        [301.6875 , 303.25   , ..., 296.42188, 296.67188]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>to3</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>260.5 260.9 263.0 ... 278.0 277.7</div><input id='attrs-69f35c24-ecf4-47f3-9b8b-2936b1441f34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69f35c24-ecf4-47f3-9b8b-2936b1441f34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34632c20-5658-46a8-b6aa-0b11b125c112' class='xr-var-data-in' type='checkbox'><label for='data-34632c20-5658-46a8-b6aa-0b11b125c112' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dobsons</dd><dt><span>long_name :</span></dt><dd>total column ozone dobsons </dd></dl></div><div class='xr-var-data'><pre>array([[[260.47394, 260.91144, ..., 257.53644, 257.6927 ],\n",
       "        [262.6302 , 263.6302 , ..., 257.97394, 258.03644],\n",
       "        ...,\n",
       "        [282.66144, 282.86456, ..., 278.66144, 277.97394],\n",
       "        [282.9427 , 283.11456, ..., 277.97394, 277.66144]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tox</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.005596 0.005605 ... 0.005964</div><input id='attrs-30b7b177-35c0-4b95-8748-55f23af614a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30b7b177-35c0-4b95-8748-55f23af614a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9a2b4da-f422-45df-9d13-179363059220' class='xr-var-data-in' type='checkbox'><label for='data-c9a2b4da-f422-45df-9d13-179363059220' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total column odd oxygen kg m-2 </dd></dl></div><div class='xr-var-data'><pre>array([[[0.005596, 0.005605, ..., 0.005533, 0.005536],\n",
       "        [0.005642, 0.005663, ..., 0.005543, 0.005544],\n",
       "        ...,\n",
       "        [0.00607 , 0.006075, ..., 0.005985, 0.00597 ],\n",
       "        [0.006077, 0.00608 , ..., 0.00597 , 0.005964]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.001081 0.0007806 ... 0.07834</div><input id='attrs-11f77d1d-bd01-4659-a6de-efee5786b965' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11f77d1d-bd01-4659-a6de-efee5786b965' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70d6c454-bfa2-446d-837b-a1c65308e6d2' class='xr-var-data-in' type='checkbox'><label for='data-70d6c454-bfa2-446d-837b-a1c65308e6d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total precipitable ice water kg m-2 </dd></dl></div><div class='xr-var-data'><pre>array([[[1.080513e-03, 7.805824e-04, ..., 0.000000e+00, 0.000000e+00],\n",
       "        [4.309416e-04, 3.956556e-04, ..., 0.000000e+00, 0.000000e+00],\n",
       "        ...,\n",
       "        [5.197525e-04, 3.308058e-05, ..., 1.525879e-02, 4.309082e-02],\n",
       "        [1.108646e-05, 0.000000e+00, ..., 3.967285e-02, 7.833862e-02]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tql</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.004391 ... 0.1669 0.1597</div><input id='attrs-1a4ecbf3-74c1-4d65-83d2-669b79c53e10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a4ecbf3-74c1-4d65-83d2-669b79c53e10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70c39f23-a92d-41f1-bf4d-2c693c4215a0' class='xr-var-data-in' type='checkbox'><label for='data-70c39f23-a92d-41f1-bf4d-2c693c4215a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total precipitable liquid water kg m-2 </dd></dl></div><div class='xr-var-data'><pre>array([[[0.000000e+00, 0.000000e+00, ..., 1.594849e-01, 1.186523e-01],\n",
       "        [0.000000e+00, 8.561183e-07, ..., 1.229858e-01, 1.127625e-01],\n",
       "        ...,\n",
       "        [6.970763e-05, 3.956258e-05, ..., 1.824341e-01, 1.308594e-01],\n",
       "        [2.108864e-07, 0.000000e+00, ..., 1.669312e-01, 1.596680e-01]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.575 7.798 15.37 ... 31.24 30.28</div><input id='attrs-19eddfaa-acb7-48c6-8450-aaab476ef823' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19eddfaa-acb7-48c6-8450-aaab476ef823' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ce45765-782f-4ebc-8941-4a875904d335' class='xr-var-data-in' type='checkbox'><label for='data-6ce45765-782f-4ebc-8941-4a875904d335' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>long_name :</span></dt><dd>total precipitable water vapor kg m-2 </dd></dl></div><div class='xr-var-data'><pre>array([[[ 7.57522 ,  7.797876, ..., 22.336939, 22.985376],\n",
       "        [ 9.532251,  9.88772 , ..., 22.243189, 23.172876],\n",
       "        ...,\n",
       "        [13.641626, 12.083033, ..., 33.74319 , 31.079126],\n",
       "        [14.364283, 12.973658, ..., 31.235376, 30.282251]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppb</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.378e+04 1.272e+04 ... 1.005e+04</div><input id='attrs-bc3d817a-f363-40df-815f-fde7ae616a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc3d817a-f363-40df-815f-fde7ae616a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79e36b5-698d-455d-8fa3-00b4d70419a7' class='xr-var-data-in' type='checkbox'><label for='data-b79e36b5-698d-455d-8fa3-00b4d70419a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>tropopause pressure based on blended estimate pa </dd></dl></div><div class='xr-var-data'><pre>array([[[13777.874, 12721.874, ..., 11825.874, 11825.874],\n",
       "        [11825.874, 11825.874, ..., 11825.874, 11825.874],\n",
       "        ...,\n",
       "        [11825.874, 11825.874, ..., 10051.874, 10051.874],\n",
       "        [11825.874, 11825.874, ..., 10051.874, 10051.874]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppt</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.378e+04 1.272e+04 ... 1.005e+04</div><input id='attrs-d631e2e4-4f84-4569-a57b-bd2f91b93050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d631e2e4-4f84-4569-a57b-bd2f91b93050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d41b41f-9599-47cb-b55b-16b65e19168a' class='xr-var-data-in' type='checkbox'><label for='data-1d41b41f-9599-47cb-b55b-16b65e19168a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>tropopause pressure based on thermal estimate pa </dd></dl></div><div class='xr-var-data'><pre>array([[[13777.574, 12721.574, ..., 11825.574, 11825.574],\n",
       "        [11825.574, 11825.574, ..., 11825.574, 11825.574],\n",
       "        ...,\n",
       "        [11825.574, 11825.574, ..., 10051.574, 10051.574],\n",
       "        [11825.574, 11825.574, ..., 10051.574, 10051.574]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppv</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.257e+03 7.427e+03 ... 8.545e+03</div><input id='attrs-0e2da8f0-6a68-4cdb-9f41-01da0298a877' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e2da8f0-6a68-4cdb-9f41-01da0298a877' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f35ec468-7380-4daa-8ffc-1222b8cb276f' class='xr-var-data-in' type='checkbox'><label for='data-f35ec468-7380-4daa-8ffc-1222b8cb276f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>pa</dd><dt><span>long_name :</span></dt><dd>tropopause pressure based on epv estimate pa </dd></dl></div><div class='xr-var-data'><pre>array([[[ 7256.5225,  7426.5225, ..., 11826.522 , 11826.522 ],\n",
       "        [ 7538.5225,  8334.522 , ..., 11826.522 , 11826.522 ],\n",
       "        ...,\n",
       "        [11826.522 , 11826.522 , ...,  8544.522 ,  8544.522 ],\n",
       "        [11826.522 , 11826.522 , ...,  8544.522 ,  8544.522 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tropq</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e1714af-ad00-48e2-a251-eafa635a6f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e1714af-ad00-48e2-a251-eafa635a6f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b840c94b-f022-41cc-ab9e-ddaa7e5be77f' class='xr-var-data-in' type='checkbox'><label for='data-b840c94b-f022-41cc-ab9e-ddaa7e5be77f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>long_name :</span></dt><dd>tropopause specific humidity using blended tropp estimate kg kg-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[9.048290e-06, 6.407059e-06, ..., 4.298544e-06, 4.211000e-06],\n",
       "        [4.486672e-06, 4.641271e-06, ..., 4.553727e-06, 4.365600e-06],\n",
       "        ...,\n",
       "        [4.585392e-06, 4.566765e-06, ..., 2.939745e-06, 2.913668e-06],\n",
       "        [4.581666e-06, 4.581666e-06, ..., 2.936951e-06, 2.912736e-06]]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tropt</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f95fe8ca-329c-4c7d-925c-12b17349462c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f95fe8ca-329c-4c7d-925c-12b17349462c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fc9a10d-d1c5-44b4-90f5-4e487f2965dd' class='xr-var-data-in' type='checkbox'><label for='data-6fc9a10d-d1c5-44b4-90f5-4e487f2965dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>tropopause temperature using blended tropp estimate k </dd></dl></div><div class='xr-var-data'><pre>array([[[204.46416, 203.304  , ..., 200.76884, 200.88994],\n",
       "        [202.13213, 202.1751 , ..., 200.53056, 200.7454 ],\n",
       "        ...,\n",
       "        [201.1165 , 200.88213, ..., 193.64775, 193.63213],\n",
       "        [201.89775, 201.60478, ..., 194.43681, 194.16338]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>286.4 285.3 289.3 ... 299.3 300.1</div><input id='attrs-18f9cf7d-8e10-440c-a6a5-9d6614a9c16e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18f9cf7d-8e10-440c-a6a5-9d6614a9c16e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03423fc3-9d51-4b4e-9aed-b49daf83e877' class='xr-var-data-in' type='checkbox'><label for='data-03423fc3-9d51-4b4e-9aed-b49daf83e877' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>k</dd><dt><span>long_name :</span></dt><dd>surface skin temperature k </dd></dl></div><div class='xr-var-data'><pre>array([[[286.4031 , 285.30154, ..., 295.16873, 295.1453 ],\n",
       "        [286.50467, 288.8406 , ..., 295.37186, 295.49686],\n",
       "        ...,\n",
       "        [306.57498, 307.63748, ..., 302.26248, 300.98123],\n",
       "        [307.04373, 307.91873, ..., 299.30936, 300.07498]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57ab3998-c4ee-4851-a800-91559d283251' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ab3998-c4ee-4851-a800-91559d283251' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79a49550-1e5f-47a4-bf2e-279d9e28d9d4' class='xr-var-data-in' type='checkbox'><label for='data-79a49550-1e5f-47a4-bf2e-279d9e28d9d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>10-meter eastward wind m s-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.292293,  1.058895, ..., -6.549503, -6.496769],\n",
       "        [ 1.868465,  1.173153, ..., -6.531925, -6.5163  ],\n",
       "        ...,\n",
       "        [-1.18671 , -1.880573, ..., -1.025334, -0.841007],\n",
       "        [-1.139347, -1.582218, ..., -1.34882 , -0.455265]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8eea2743-d7e9-43cf-a60d-58a2847025e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eea2743-d7e9-43cf-a60d-58a2847025e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84d9d5a3-3d39-491c-94bb-e8c6838b38f4' class='xr-var-data-in' type='checkbox'><label for='data-84d9d5a3-3d39-491c-94bb-e8c6838b38f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>2-meter eastward wind m s-1n </dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.121398,  0.44464 , ..., -5.68036 , -5.627626],\n",
       "        [ 1.243468,  0.739562, ..., -5.668642, -5.645204],\n",
       "        ...,\n",
       "        [-1.047792, -1.627748, ..., -0.513368, -0.408876],\n",
       "        [-1.002138, -1.385194, ..., -0.925477, -0.271181]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u50m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-862df62f-0b39-42e7-ba35-ddd470958588' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-862df62f-0b39-42e7-ba35-ddd470958588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e5f27dc-bc30-4d81-a8af-9f61ea3dc8d4' class='xr-var-data-in' type='checkbox'><label for='data-8e5f27dc-bc30-4d81-a8af-9f61ea3dc8d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>eastward wind at 50 meters m s-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.469079,  1.549157, ..., -7.223304, -7.154945],\n",
       "        [ 2.530602,  1.681969, ..., -7.197914, -7.178382],\n",
       "        ...,\n",
       "        [-1.278724, -1.973976, ..., -1.3339  , -1.0568  ],\n",
       "        [-1.221839, -1.651161, ..., -1.591224, -0.564125]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-417fb437-93e6-4bbc-89f5-0e83e76e936c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-417fb437-93e6-4bbc-89f5-0e83e76e936c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8106d375-04cc-4c53-8728-7405649aaf30' class='xr-var-data-in' type='checkbox'><label for='data-8106d375-04cc-4c53-8728-7405649aaf30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>10-meter northward wind m s-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[ 1.388269,  1.26034 , ...,  3.990808,  4.137293],\n",
       "        [ 1.139246,  1.638269, ...,  4.037683,  4.277918],\n",
       "        ...,\n",
       "        [ 0.422449, -0.779943, ...,  1.627527,  1.357996],\n",
       "        [ 1.019617,  0.01742 , ...,  1.703699,  1.633387]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v2m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d364339b-32ce-4baa-b738-d79f2e2d2046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d364339b-32ce-4baa-b738-d79f2e2d2046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-291284b1-778b-4920-82af-7921df5bff79' class='xr-var-data-in' type='checkbox'><label for='data-291284b1-778b-4920-82af-7921df5bff79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>2-meter northward wind m s-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.632959,  0.533837, ...,  3.461572,  3.582665],\n",
       "        [ 0.75747 ,  1.036279, ...,  3.50454 ,  3.705712],\n",
       "        ...,\n",
       "        [ 0.373193, -0.666701, ...,  0.898584,  0.624658],\n",
       "        [ 0.898095,  0.022607, ...,  1.141259,  1.012841]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v50m</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-183b475e-574e-4164-a620-a324c547ce1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-183b475e-574e-4164-a620-a324c547ce1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14f9c0bb-13b2-4382-b404-c0d6ac08ee0f' class='xr-var-data-in' type='checkbox'><label for='data-14f9c0bb-13b2-4382-b404-c0d6ac08ee0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>northward wind at 50 meters m s-1 </dd></dl></div><div class='xr-var-data'><pre>array([[[ 2.207321,  2.043259, ...,  4.31572 ,  4.491501],\n",
       "        [ 1.672165,  2.323532, ...,  4.368454,  4.636032],\n",
       "        ...,\n",
       "        [ 0.421189, -0.837845, ...,  2.083298,  1.789353],\n",
       "        [ 1.06279 , -0.005569, ...,  2.03154 ,  1.994431]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5fa65ef0-3960-40d8-bcf9-40bec88da3b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5fa65ef0-3960-40d8-bcf9-40bec88da3b6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>MERRA-2 inst1_2d_asm_Nx: 2d,1-Hourly,Instantaneous,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4</dd><dt><span>Conventions :</span></dt><dd>COARDS\n",
       "GrADS</dd><dt><span>dataType :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Thu Jan 19 02:15:29 GMT 2023 : imported by GrADS Data Server 2.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 1, lat: 101, lon: 113)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2017-05-27T06:00:00\n",
       "  * lat          (lat) float64 -25.0 -24.5 -24.0 -23.5 ... 23.5 24.0 24.5 25.0\n",
       "  * lon          (lon) float64 30.0 30.62 31.25 31.88 ... 98.75 99.38 100.0\n",
       "    spatial_ref  int64 0\n",
       "Data variables: (12/24)\n",
       "    disph        (time, lat, lon) float32 0.3308 9.457 7.119 ... 2.465 4.119\n",
       "    ps           (time, lat, lon) float32 8.702e+04 8.712e+04 ... 8.073e+04\n",
       "    qv10m        (time, lat, lon) float32 0.005655 0.005672 ... 0.01127 0.01074\n",
       "    qv2m         (time, lat, lon) float32 0.007309 0.007129 ... 0.01175 0.01109\n",
       "    slp          (time, lat, lon) float32 1.013e+05 1.013e+05 ... 1.005e+05\n",
       "    t10m         (time, lat, lon) float32 287.6 287.4 289.6 ... 295.4 295.6\n",
       "    ...           ...\n",
       "    u10m         (time, lat, lon) float32 ...\n",
       "    u2m          (time, lat, lon) float32 ...\n",
       "    u50m         (time, lat, lon) float32 ...\n",
       "    v10m         (time, lat, lon) float32 ...\n",
       "    v2m          (time, lat, lon) float32 ...\n",
       "    v50m         (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:        MERRA-2 inst1_2d_asm_Nx: 2d,1-Hourly,Instantaneous,Single-L...\n",
       "    Conventions:  COARDS\\nGrADS\n",
       "    dataType:     Grid\n",
       "    history:      Thu Jan 19 02:15:29 GMT 2023 : imported by GrADS Data Serve..."
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the time of the blob \n",
    "dsrb = dsr.sel(time=blob.time).rio.set_spatial_dims('lon','lat',inplace=True)\n",
    "dsrb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "524b2a08-258a-486c-b14c-e4c4609925f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x16f5c0430>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CLIP \n",
    "\n",
    "dsrblob = dsrb.rio.clip( [blob.geometry], crs=4326 ) #TypeError: 'Polygon' object is not iterable SO PUT IT IN BRACKETS []\n",
    "# All values above 55mm as expected! \n",
    "dsrblob.tqv.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1073b350-3afc-4c42-8b50-cfe737c17915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x16f674d00>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blobuf = blob.geometry.boundary.buffer(-2, single_sided=True) # negative values are outside the blob \n",
    "\n",
    "dsrblobuf = dsrb.rio.clip( [blobuf], crs=4326 ) #TypeError: 'Polygon' object is not iterable SO PUT IT IN BRACKETS []\n",
    "dsrblobuf.tqi.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "45098caa-a4ab-4eb0-9d22-1952c2d54bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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      ],
      "text/plain": [
       "<POLYGON ((36.929 -2.255, 37.042 -1.952, 37.165 -1.669, 37.311 -1.373, 37.44...>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subtract shapes to get a buffer from 1-3 degrees beyond the blob \n",
    "blobuf31 = blob.geometry.boundary.buffer(-3, single_sided=True)-blobuf \n",
    "blobuf31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b090299d-3c9b-47f3-a998-68adc139afc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x16bcc7e80>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsrblobuf31 = dsrb.rio.clip( [blobuf31], crs=4326 ) \n",
    "dsrblobuf31.tqv.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be2b0ef2-534c-4ed7-a4e4-5cd9aac952b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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       "\n",
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       "\n",
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       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
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       "\n",
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       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
       ".xr-attrs,\n",
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       "\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      ()\n",
       "Coordinates:\n",
       "    spatial_ref  int64 0\n",
       "Data variables: (12/24)\n",
       "    disph        float32 1.133\n",
       "    ps           float32 9.838e+04\n",
       "    qv10m        float32 0.01652\n",
       "    qv2m         float32 0.01693\n",
       "    slp          float32 1.014e+05\n",
       "    t10m         float32 299.0\n",
       "    ...           ...\n",
       "    u10m         float32 -2.089\n",
       "    u2m          float32 -1.776\n",
       "    u50m         float32 -2.394\n",
       "    v10m         float32 4.123\n",
       "    v2m          float32 3.266\n",
       "    v50m         float32 4.797</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fc4f4fed-dc73-4154-aae5-952a3788bdbc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc4f4fed-dc73-4154-aae5-952a3788bdbc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e2a001ce-854b-46a6-9585-bb240b34a612' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2a001ce-854b-46a6-9585-bb240b34a612' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c8f6f7d0-9c7a-459c-8ba2-8bb816d8d6a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8f6f7d0-9c7a-459c-8ba2-8bb816d8d6a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3acc1e34-3408-4254-9d22-4f0ef75f9c8b' class='xr-var-data-in' type='checkbox'><label for='data-3acc1e34-3408-4254-9d22-4f0ef75f9c8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>35.9375 0.625 0.0 -10.75 0.0 0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5aa80fa-1224-4ce0-a758-2d0a2f346d6a' class='xr-section-summary-in' type='checkbox'  ><label for='section-a5aa80fa-1224-4ce0-a758-2d0a2f346d6a' class='xr-section-summary' >Data variables: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>disph</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.133</div><input id='attrs-567be843-8408-412c-b8de-c744c362d23f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-567be843-8408-412c-b8de-c744c362d23f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-041720fd-2b09-4c8a-8270-90390ca11964' class='xr-var-data-in' type='checkbox'><label for='data-041720fd-2b09-4c8a-8270-90390ca11964' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.1325457, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ps</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.838e+04</div><input id='attrs-dc1feb28-7c10-496a-b59e-1c116446d629' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc1feb28-7c10-496a-b59e-1c116446d629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e482ffa-b227-4812-8775-a7641257e749' class='xr-var-data-in' type='checkbox'><label for='data-7e482ffa-b227-4812-8775-a7641257e749' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(98377.98, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qv10m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01652</div><input id='attrs-74b0b9b0-c9a0-4d52-b6df-3d8f46a11dbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74b0b9b0-c9a0-4d52-b6df-3d8f46a11dbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-884b43d8-70de-4822-8529-168100dd56c2' class='xr-var-data-in' type='checkbox'><label for='data-884b43d8-70de-4822-8529-168100dd56c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.01651979, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qv2m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01693</div><input id='attrs-a64b27f9-8640-44bf-bbd8-db08eb1f41df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a64b27f9-8640-44bf-bbd8-db08eb1f41df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a379245-7fae-4040-86c9-d0da9c04fa59' class='xr-var-data-in' type='checkbox'><label for='data-5a379245-7fae-4040-86c9-d0da9c04fa59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.01692833, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.014e+05</div><input id='attrs-ac6b4b94-5821-4fea-a565-6de6ef6547ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac6b4b94-5821-4fea-a565-6de6ef6547ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33804097-0c8e-4783-9e5c-af7bc07e0518' class='xr-var-data-in' type='checkbox'><label for='data-33804097-0c8e-4783-9e5c-af7bc07e0518' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(101397.33, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t10m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>299.0</div><input id='attrs-97377d2d-759d-4ef2-9dad-2e914103767d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97377d2d-759d-4ef2-9dad-2e914103767d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c07c23fc-74d1-4cad-ba11-a16935671ccb' class='xr-var-data-in' type='checkbox'><label for='data-c07c23fc-74d1-4cad-ba11-a16935671ccb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(299.00717, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t2m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>299.3</div><input id='attrs-39bfa526-6c1d-4f11-9ee8-b117847e3c54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39bfa526-6c1d-4f11-9ee8-b117847e3c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ba1feb7-1422-4b39-8b45-c22f35231ca6' class='xr-var-data-in' type='checkbox'><label for='data-2ba1feb7-1422-4b39-8b45-c22f35231ca6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(299.30457, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>to3</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>254.9</div><input id='attrs-6893dfc3-b46b-4a59-8ec1-a205f983666e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6893dfc3-b46b-4a59-8ec1-a205f983666e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1db95115-4551-4c0c-91c6-53c7728f978d' class='xr-var-data-in' type='checkbox'><label for='data-1db95115-4551-4c0c-91c6-53c7728f978d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(254.8643, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.005475</div><input id='attrs-e0d81d07-9b52-4538-93ce-56a2a7f51cea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0d81d07-9b52-4538-93ce-56a2a7f51cea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39474935-7223-45ac-a1e7-b1e81903b1ae' class='xr-var-data-in' type='checkbox'><label for='data-39474935-7223-45ac-a1e7-b1e81903b1ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.00547488, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01209</div><input id='attrs-ec050b01-3e45-44ad-8b50-d9a4fa7be44b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec050b01-3e45-44ad-8b50-d9a4fa7be44b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3445f50-c2e0-4cf8-a589-f096e62df73c' class='xr-var-data-in' type='checkbox'><label for='data-a3445f50-c2e0-4cf8-a589-f096e62df73c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.01209443, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tql</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0765</div><input id='attrs-b891026d-e4da-4628-97c6-22f141a0925f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b891026d-e4da-4628-97c6-22f141a0925f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c1c71d-039d-48fe-9d01-de6d8dd67faa' class='xr-var-data-in' type='checkbox'><label for='data-07c1c71d-039d-48fe-9d01-de6d8dd67faa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.07650363, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>44.12</div><input id='attrs-67df8fe4-bdd3-4581-9620-45f04042e30a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67df8fe4-bdd3-4581-9620-45f04042e30a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1522bb9-61b1-49cb-967b-9bbc3692a576' class='xr-var-data-in' type='checkbox'><label for='data-e1522bb9-61b1-49cb-967b-9bbc3692a576' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(44.119328, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppb</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.005e+04</div><input id='attrs-35832e3d-10f6-4621-8329-dec98e7c1f10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35832e3d-10f6-4621-8329-dec98e7c1f10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-294ea4d7-b160-4c71-b09b-62d9a31e9869' class='xr-var-data-in' type='checkbox'><label for='data-294ea4d7-b160-4c71-b09b-62d9a31e9869' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(10051.873, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.005e+04</div><input id='attrs-1d8006ab-5d7e-4991-bbbe-bd91cb2c0f9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d8006ab-5d7e-4991-bbbe-bd91cb2c0f9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddff655a-3151-4e99-8ea6-10ea1d9539ff' class='xr-var-data-in' type='checkbox'><label for='data-ddff655a-3151-4e99-8ea6-10ea1d9539ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(10051.573, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>troppv</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.318e+03</div><input id='attrs-5cdd6d63-fe67-4aad-9c83-54bff6ae2bf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cdd6d63-fe67-4aad-9c83-54bff6ae2bf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fdfbb8f-9bd5-4268-9562-6164c5d025a0' class='xr-var-data-in' type='checkbox'><label for='data-9fdfbb8f-9bd5-4268-9562-6164c5d025a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(7318.114, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tropq</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.86e-06</div><input id='attrs-2785a24f-a5be-4c08-8d6e-92ac9c9226ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2785a24f-a5be-4c08-8d6e-92ac9c9226ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e57dc3de-7e55-48f8-a9ed-be5c8059166b' class='xr-var-data-in' type='checkbox'><label for='data-e57dc3de-7e55-48f8-a9ed-be5c8059166b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2.860004e-06, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tropt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>193.5</div><input id='attrs-0c2e335d-3a4b-4e49-b731-529fa2d1bb08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c2e335d-3a4b-4e49-b731-529fa2d1bb08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0a812b0-8f06-4ef4-b29e-53d7b9c1aa6b' class='xr-var-data-in' type='checkbox'><label for='data-f0a812b0-8f06-4ef4-b29e-53d7b9c1aa6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(193.5453, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>300.4</div><input id='attrs-f71fc382-c185-4652-8963-e1fdd94cebbb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f71fc382-c185-4652-8963-e1fdd94cebbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c90819c0-bf1a-46c9-8a3b-e5c99f31aa0a' class='xr-var-data-in' type='checkbox'><label for='data-c90819c0-bf1a-46c9-8a3b-e5c99f31aa0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(300.38885, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u10m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.089</div><input id='attrs-24b62e76-bd2b-47d1-b863-9d6429c71af9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24b62e76-bd2b-47d1-b863-9d6429c71af9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28591e1f-fd92-4440-97aa-8a28c0e1f46f' class='xr-var-data-in' type='checkbox'><label for='data-28591e1f-fd92-4440-97aa-8a28c0e1f46f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(-2.0894673, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u2m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.776</div><input id='attrs-fdd23a37-7e79-4e57-a158-d2e30aa06aec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdd23a37-7e79-4e57-a158-d2e30aa06aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f48aa144-77ca-4335-bdda-fbe7b5498947' class='xr-var-data-in' type='checkbox'><label for='data-f48aa144-77ca-4335-bdda-fbe7b5498947' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(-1.7759961, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u50m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.394</div><input id='attrs-ba6da9f2-c102-4a9d-9f66-e5dc91a0b59b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba6da9f2-c102-4a9d-9f66-e5dc91a0b59b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2e681ae-1ac6-4eaf-a29b-6f58382a50ae' class='xr-var-data-in' type='checkbox'><label for='data-a2e681ae-1ac6-4eaf-a29b-6f58382a50ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(-2.3939443, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v10m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.123</div><input id='attrs-f23a4347-c057-4bd7-95d9-18408e5396cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f23a4347-c057-4bd7-95d9-18408e5396cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42794fc9-1478-4b20-b428-118f2ec10cca' class='xr-var-data-in' type='checkbox'><label for='data-42794fc9-1478-4b20-b428-118f2ec10cca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4.122896, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v2m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.266</div><input id='attrs-bc343e02-2ffe-4044-a60a-336dbeba8519' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc343e02-2ffe-4044-a60a-336dbeba8519' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1a04122-1b5d-4b00-890d-b2e73f1e0d88' class='xr-var-data-in' type='checkbox'><label for='data-e1a04122-1b5d-4b00-890d-b2e73f1e0d88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3.2661884, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v50m</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.797</div><input id='attrs-28ff9e42-bf47-4b38-9b8b-98a512b43ac2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28ff9e42-bf47-4b38-9b8b-98a512b43ac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c52bfa8f-9f31-4688-86f5-863a57cc18fe' class='xr-var-data-in' type='checkbox'><label for='data-c52bfa8f-9f31-4688-86f5-863a57cc18fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4.7965426, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23d1b4f9-3bcf-4850-8ac8-fb1a15667296' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-23d1b4f9-3bcf-4850-8ac8-fb1a15667296' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      ()\n",
       "Coordinates:\n",
       "    spatial_ref  int64 0\n",
       "Data variables: (12/24)\n",
       "    disph        float32 1.133\n",
       "    ps           float32 9.838e+04\n",
       "    qv10m        float32 0.01652\n",
       "    qv2m         float32 0.01693\n",
       "    slp          float32 1.014e+05\n",
       "    t10m         float32 299.0\n",
       "    ...           ...\n",
       "    u10m         float32 -2.089\n",
       "    u2m          float32 -1.776\n",
       "    u50m         float32 -2.394\n",
       "    v10m         float32 4.123\n",
       "    v2m          float32 3.266\n",
       "    v50m         float32 4.797"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All the fields in the 1-3 degree outer buffer of the blob! \n",
    "dsrblobuf31.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e362d905-5e0b-4e94-9a4d-65112da1129f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1c51c9ac-e315-4a2a-af59-8aa39c253ee7",
   "metadata": {},
   "source": [
    "# Construct distance-from-boundary fields instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "0fbf8631-5724-446c-838c-c253f67c5918",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;distance&#x27; (lat: 101, lon: 113)&gt;\n",
       "array([[ 0.330811,  9.457031,  7.119141, ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 0.310303,  0.715332,  2.089844, ...,  0.      ,  0.      ,  0.      ],\n",
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       "       ...,\n",
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       "       [ 0.054733,  0.054703,  0.060394, ...,  5.662109,  2.464844,  4.119141]],\n",
       "      dtype=float32)\n",
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       "Attributes:\n",
       "    units:      m\n",
       "    long_name:  zero plane displacement height m </pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'distance'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 101</li><li><span class='xr-has-index'>lon</span>: 113</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1023902d-6642-427a-a42d-e40a3d2b8c83' class='xr-array-in' type='checkbox' checked><label for='section-1023902d-6642-427a-a42d-e40a3d2b8c83' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.3308 9.457 7.119 0.3087 0.2965 ... 16.57 16.66 5.662 2.465 4.119</span></div><div class='xr-array-data'><pre>array([[ 0.330811,  9.457031,  7.119141, ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 0.310303,  0.715332,  2.089844, ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 7.177734,  0.333618,  0.273193, ...,  0.      ,  0.      ,  0.      ],\n",
       "       ...,\n",
       "       [ 0.053589,  0.053162,  0.05629 , ..., 16.382812,  6.154297,  4.927734],\n",
       "       [ 0.054718,  0.054535,  0.057541, ..., 14.957031,  4.068359,  5.041016],\n",
       "       [ 0.054733,  0.054703,  0.060394, ...,  5.662109,  2.464844,  4.119141]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-2242ac2e-0a0c-46d3-b2c6-9ab3b76684fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2242ac2e-0a0c-46d3-b2c6-9ab3b76684fd' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-05-27T06:00:00</div><input id='attrs-87947539-c5a8-4f0c-9432-a062002f9276' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87947539-c5a8-4f0c-9432-a062002f9276' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce41e838-c7ab-44e7-bc18-23dab87efa42' class='xr-var-data-in' type='checkbox'><label for='data-ce41e838-c7ab-44e7-bc18-23dab87efa42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>376200</dd><dt><span>grads_min :</span></dt><dd>00z01jan1980</dd><dt><span>grads_step :</span></dt><dd>60mn</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>00z01jan1980</dd><dt><span>maximum :</span></dt><dd>23z30nov2022</dd><dt><span>resolution :</span></dt><dd>0.041666668</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2017-05-27T06:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-25.0 -24.5 -24.0 ... 24.5 25.0</div><input id='attrs-d7e9e9a1-176c-4211-b757-ba666484f037' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7e9e9a1-176c-4211-b757-ba666484f037' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da15e110-659b-456d-a697-39b53c7e211e' class='xr-var-data-in' type='checkbox'><label for='data-da15e110-659b-456d-a697-39b53c7e211e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>361</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-90.0</dd><dt><span>maximum :</span></dt><dd>90.0</dd><dt><span>resolution :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-25. , -24.5, -24. , -23.5, -23. , -22.5, -22. , -21.5, -21. , -20.5,\n",
       "       -20. , -19.5, -19. , -18.5, -18. , -17.5, -17. , -16.5, -16. , -15.5,\n",
       "       -15. , -14.5, -14. , -13.5, -13. , -12.5, -12. , -11.5, -11. , -10.5,\n",
       "       -10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,  -5.5,\n",
       "        -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,  -1. ,  -0.5,\n",
       "         0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,   4.5,\n",
       "         5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,   9. ,   9.5,\n",
       "        10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,  13.5,  14. ,  14.5,\n",
       "        15. ,  15.5,  16. ,  16.5,  17. ,  17.5,  18. ,  18.5,  19. ,  19.5,\n",
       "        20. ,  20.5,  21. ,  21.5,  22. ,  22.5,  23. ,  23.5,  24. ,  24.5,\n",
       "        25. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>30.0 30.62 31.25 ... 99.38 100.0</div><input id='attrs-823e0299-80c4-4de0-a46a-97379199cd13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-823e0299-80c4-4de0-a46a-97379199cd13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-297857cd-5d73-47d7-819e-6026e556f263' class='xr-var-data-in' type='checkbox'><label for='data-297857cd-5d73-47d7-819e-6026e556f263' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>576</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>-180.0</dd><dt><span>maximum :</span></dt><dd>179.375</dd><dt><span>resolution :</span></dt><dd>0.625</dd></dl></div><div class='xr-var-data'><pre>array([ 30.   ,  30.625,  31.25 ,  31.875,  32.5  ,  33.125,  33.75 ,  34.375,\n",
       "        35.   ,  35.625,  36.25 ,  36.875,  37.5  ,  38.125,  38.75 ,  39.375,\n",
       "        40.   ,  40.625,  41.25 ,  41.875,  42.5  ,  43.125,  43.75 ,  44.375,\n",
       "        45.   ,  45.625,  46.25 ,  46.875,  47.5  ,  48.125,  48.75 ,  49.375,\n",
       "        50.   ,  50.625,  51.25 ,  51.875,  52.5  ,  53.125,  53.75 ,  54.375,\n",
       "        55.   ,  55.625,  56.25 ,  56.875,  57.5  ,  58.125,  58.75 ,  59.375,\n",
       "        60.   ,  60.625,  61.25 ,  61.875,  62.5  ,  63.125,  63.75 ,  64.375,\n",
       "        65.   ,  65.625,  66.25 ,  66.875,  67.5  ,  68.125,  68.75 ,  69.375,\n",
       "        70.   ,  70.625,  71.25 ,  71.875,  72.5  ,  73.125,  73.75 ,  74.375,\n",
       "        75.   ,  75.625,  76.25 ,  76.875,  77.5  ,  78.125,  78.75 ,  79.375,\n",
       "        80.   ,  80.625,  81.25 ,  81.875,  82.5  ,  83.125,  83.75 ,  84.375,\n",
       "        85.   ,  85.625,  86.25 ,  86.875,  87.5  ,  88.125,  88.75 ,  89.375,\n",
       "        90.   ,  90.625,  91.25 ,  91.875,  92.5  ,  93.125,  93.75 ,  94.375,\n",
       "        95.   ,  95.625,  96.25 ,  96.875,  97.5  ,  98.125,  98.75 ,  99.375,\n",
       "       100.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-c8ae7f6b-3902-4215-abdd-285ad9aedb9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8ae7f6b-3902-4215-abdd-285ad9aedb9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7baa3015-7fd5-4355-880e-fd5f07a515da' class='xr-var-data-in' type='checkbox'><label for='data-7baa3015-7fd5-4355-880e-fd5f07a515da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6da84f13-367a-40fd-8807-f2b4fb7be9d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6da84f13-367a-40fd-8807-f2b4fb7be9d5' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>zero plane displacement height m </dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'distance' (lat: 101, lon: 113)>\n",
       "array([[ 0.330811,  9.457031,  7.119141, ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 0.310303,  0.715332,  2.089844, ...,  0.      ,  0.      ,  0.      ],\n",
       "       [ 7.177734,  0.333618,  0.273193, ...,  0.      ,  0.      ,  0.      ],\n",
       "       ...,\n",
       "       [ 0.053589,  0.053162,  0.05629 , ..., 16.382812,  6.154297,  4.927734],\n",
       "       [ 0.054718,  0.054535,  0.057541, ..., 14.957031,  4.068359,  5.041016],\n",
       "       [ 0.054733,  0.054703,  0.060394, ...,  5.662109,  2.464844,  4.119141]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time         datetime64[ns] 2017-05-27T06:00:00\n",
       "  * lat          (lat) float64 -25.0 -24.5 -24.0 -23.5 ... 23.5 24.0 24.5 25.0\n",
       "  * lon          (lon) float64 30.0 30.62 31.25 31.88 ... 98.75 99.38 100.0\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    units:      m\n",
       "    long_name:  zero plane displacement height m "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For blob time (dsrb), repurpose the disph variable as a container, renaming it \n",
    "\n",
    "result = dsrb.rename({\"disph\":\"distance\"}).distance.squeeze()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a3e27961-eac2-47c4-b436-1770017cfc85",
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import Polygon, LineString, Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "02c6a809-8d62-4548-ae68-2680936e1a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "lat2d = dsr.lat.values[:,None]   + dsr.lon.values*0\n",
    "lon2d = dsr.lat.values[:,None]*0 + dsr.lon.values\n",
    "points = gp.GeoSeries( [Point(x, y) for x, y in zip(lon2d.ravel(),lat2d.ravel()) ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "e51e7765-9555-4c4f-8467-a4ddb867b479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x179124910>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/eiJWEvQW8eMkjFoq6OIEaBthNrbPxNLQ0NAwRyjKetsKM7G9JpawB1/wCqthJT0mMq6u1/dsUYyboZJ5mC8x6BzGirXUt/Qe2GwmYzEatS/HxKT6i0SeScPFcj+2oadN4P4MYltLHmNxfXdVyZOrNmbF0Fp6+okxzpDVGLpF3Dw31tAYO/Zj6ilRZ72YGMsrrM9mqmJbBsbrsOJYQlkxxqWgycReZHG7TUIV1tq8YmJ7TSwNDQ0Nc4OwvlT73/PDtppYuoVJHoFvZOGrZiUlL7KucD7LliJbitv5+14EugUzWr7gTaRGWeEyU5MZhL32gv4y6DOQYQ5DQ5uJvbVIV79xJP1QKxntMZZqBWmdxWp619NfNCf9GmShylOs0itMeyzDGMdgFF28hxjvfZYZIbm2iy+x7IrYZVFHGc0yinVWNH5Wl2o509NYlCQUqyHCtppYGhoaGuaJxlhstImloaGhYQNQ2sQyCttnYhGiLbDc3XjSrbC4LAsQNK4bJ6aXrg0obVvVboEZvY7t2zxB1NweM/Zv8kuNbTJze8wKqKsR9rvAuui6LH2LK+sudHdRX5p5rFquyDEki79LWvRE9ajK2qLKyuxt1axt3Ie1i2NtX2XtJRG7s23iZKuq4OxiOhUEV1/t22ykgMldwzVJzZKWiSnG+07jz6wmzUuydTbdiWBQs928G2L7TCwNDQ0Nc8QAYSerizZjS2J7TSxdYFQNY0mvSbqJyvJVZYnpmCZVC+4pu9g4Sxndd9x/V2cRFrOsH4g4rCywGYvFWMkOM/fk5LKCL4alhHcBlUaAZJfIMjyPI9zCyttY1XYOEZUp9S0U2+XJLitZg1rsL3cSSFx+/X0u4kdlJsPp0r3Qq1NfZ9sQ//YCK/FPTVfk7Hl0XVVKfeineYkz5dduL1SiMRYb22tiaWhoaJgTmsYyGgudWETkUOC/gCvg1mwnquq/i8gBwBuBw4FzgD9R1V+UOzOYSh4wCUNWI0ZdZ1edJlP6Sm2YcczYdbnXu/TrLIbTMYlaNmOwGB2E/XPNq3oGiZGgMLgIixVYZ5TlzCV5IVkQZWJBnM6/c3H1d/GHnr3sIsuw2hntu7fQYhkFLSdhWTnFS7SP8HlaekpgFIZeU7A511ri12GmzTc+s2JK/cy+hOpUpHmJ0/lLVfRwLYT1kv/5boxFvyu7gL9R1WsBNwGOF5FrA08CPqSqRwIf8s8bGhoatgwUGLBSddvdsFDGoqo/An7kH18oIl8FDgaOBW7lm70W+CjwxDG9FRhLXzMRywMG+u2NMqtdv65s7aJgeoF1dX2js7hFc4lvsZmyJmP0VdJfBkE7S5lL3H+S0iVLyx57mAUto2sfr6itlC4YfeRtKoInXV++HfmDYaX5vckSYFpaRuqRVWIlWXuDGaXMKxg2muF0WkvimeW7jPUtza8rMM+YsVraSqku12SsYMspoW2F2dgyGouIHA7cAPg/4PJ+0kFVfyQilxtxzXHAcQCrB156TpY2NDQ0uIXYmjavMAtbYmIRkX2BtwJ/raq/kspVhaqeCJwIsNdVDh5ma/BLoxWDseSaSboAG81KLJNWKmJcZoFx49SkcrHaWOkpurgSI14ilFlsxmIxndQxqf6SJUxM41L6nlwdGxkYNmcpSsSMiRgWTaKLJO+otcjO+zKOOTa1rOy6Yp/W2Gb7/ntTjKHpdgGi9z5jRGLqQn2xxEqm2UvzUkrpYsW4xMhT9iftp5vSZX033OaqwcInFhHZgZtUTlLV//bFPxGRK3q2ckXgp4uzsKGhocFCE+9HYdFeYQK8Eviqqj4/qjoVeBDwbH//9vF9gXj/9VxHkSSOIasrxKzAkJXUaCyz1lVyhjQarl3Jx97SWsTSWHxZDZtJJJPQVeSRNcwXWKG/DPpF3adnxaUM+qxEeu5U0QuRvs1iaDJDMmb0lbexYlzM2A7f3liUm3Ep2fVJen7DrKF+kmktVh8JM8w7iHWawE6N34vhyWXGsWQJPa2U+l3kfeJNFthsYJvxG5d3GhlmHQI2xaO5gnjf0MeiGcvNgAcCXxKRL/iyJ+MmlDeJyMOA7wH3WYx5DQ0NDaOx3gIkTSzaK+yTWEtAh9tO2t/KiIj7lWSVlWomVl2MUvvSdaZ9hdjsQcG7pHRdCSFYutR3qrX0mY52EfHj2Ux6DLGvs7yOavQX4/jdISPoexOVdBcx3K+smBhLFuhrLNFrrIlxMfN7Ge1zFhNft562SewzmEeuBxU1E+toYov9WK8xeOmpGnX+PmEX+TjGG2ZF3udlVu6v0kFfcd0UtxUUYU0XvTbfmmjvSkNDQ8MG0MT70WgTS0NDQ8MGoEjbChuB7TOxyFB8zQX6lUjszcV4S7wvbXdZ21Ildl0ruK8WBPd60d5G6XI1X0+8FZZuTZW2yeJttU5LN0T4KmE/XgkO0m2SRCSvEfTNc9p9X/F1xsmWedLKdOTMCcHajTFMrdoeM7eV+nWldDJVp1hadUnGlNCJjmzffUOSuiCcx1uHocoS+3PR3trbs7bQ8jYRcrdjmH7a/MZYTLR3paGhoWEDUIV1Xam6bQWISM8JyiqbBrYNYxHUYCOuLl7xr2QuyZZ4X2Ilk4r3k7KNWcTxxn3mjMgKlIxNzhmNxWYs8b5T7eMgwMw92RL2hyk9DN9dw0W6RtCPX4Pkj6xz6hOtP1slW8K2mQrGI0rZXgy2zER4k0kYz3M35biPYUDlaIG+mL4ltqNG7DfqSmlekvc5E+irDwHrGFUhMaVxCNh0IEXHmC2IE4A3V5RtGttmYmloaGiYJxTYOQevMM8qngZcC7ixqp4e1Z0APAy3hHm0qr7PuP5OwJ2Bg0XkhVHVfrhEwFPHtppYgpZS0lFWszQvMTup0lgqGctmdZFZIXcbthmLpbGEuuja4LIbVpRJ28HIvkr6y8ByUw3sJVQaQXem7mK4tQ5ZQ9Bt+i6vZtJKQ5PIXZBTZpDaB8OFdJ0bsFGXay0j+uoxokk1FrOvwBr6wZaSuR0nXZTSvBhsadOHgMWPTZFpelBkXgd9fRm4J/CyuNBngr8fcBRwJeCDInJ1VV3Prv8hcDpwd+CMqPxC4LGzMHhbTSwNDQ0N88Q83I1V9auQbUM6HAucoqoXA2eLyLeAGwOfzq4/EzhTRE5W1bWZG8w2m1gCSyjpKKtSobEUgiYTvaYQuDivhJTBntqV00q+J5wszv0ediEY0mIgw+OEYyax4u2K+/BPDP2lyxdI2iTuf4CRCyTzGIOIvRgBkp1nWnitsYFmYka/krYCMbMHtQd9VbGS+H2zmEd2nRXUabGmXEgybYibZ/ZYmowafZmJJgMjNAhLp5+YA+VllsdY364O1rHFU4ACg3ph/kAROT16fqJPorsZHAycFj0/15eNwo1F5GnAlXH//QKoql5lk3b0sK0mloaGhob5QSY5j+U8VT1mZE8iH8SdpJvjKar69pEG9FFa0b4St/V1BolbyfSxbSYWkSFTqdFRVn3bcYwlZyUxE8l1lFnrKpP2X2IxVl1gIz1WAwzCitJkLO5+PaIZq0FjMWNi+vpLWFb3WA1DJhU0tAGGx1jCPMJ44XnsFeTrrLQiBZYxTPZorJa7g7gMmlGpffRYRq1mkttutZtQYzE9v3I7k/b+eeKt1b9+mGDSP7cOATN0lDwxZeIxVpOY0krBPwVMyFjKfanebgOXnQscGj0/BKenjMIFqvqeDYwzMbbNxNLQ0NAwT2yBg75OBU4WkefjxPsjgc/kjUTkhv7hR0TkecB/AxeHelX93LQN20YTi/aYympBM7EYywrp9Xl9r/0GGUsYZ1If+EmTUQ4KqzOLsYSyWjZjsZiAXH+BIfde9Yu89Yh4hM8jsJ6VeC/cl3UaUEJ0zBzsqS1WOvuaGJe4L+NogNxTLLXLCEjJWUKJlRgvxxpnyMpiw9L7VMtIB7QOwSuyEkP7KB0olrzGXD8y2WJgJcZ11kdtHWUdkB3qNrLdJjCP4EcRuQfwIuAg4F0i8gVVvYOqniUibwK+gnMbPt7wCAP41+x5vCWnwG2mbfM2mlgaGhoa5gdl8sXhhsZRfRvwthF1zwKeNeb6W8/CrhLaxNLQ0NCwISzXCZIi8jij+ALgDFX9wjTH2jYTi9B3JS4J9Ht0bftbW9Z2l7XNlW9NjdsKK9WXhPbNOgWY214Fgd661tom622PJelbjG0yv6XVbT9FbqBD7T3dEnPN0gDJJBVMSBxpiepFsbsieDKq79yNE5E466MkxkePzZQuhW2y/DozGDJGQaCvOavFdCnutvasvsKD/rZajNyl2Ngl7Lsdx/0bKVrMsnwLbGYBkvVu/lsEx/jbO/zzuwCfBR4hIm9W1edOa6BtM7E0NDQ0zBPuoK+FiveT4rLADVX11wAi8lTgLcAtcS7IbWLJIdJ3M64R6PeIRGKTsVBgLAU2k7epxSzS5te6HZeYTWAzVvtdnkLsEamkNcJ+ks480J2OjQzrAnsZuo9H9vVjLYunmtcET5pL6YoVvskMIpfa3AW5LJL360ruw2os8Itp863nE9pVc7pkKpwHpiK99r3ElMYHOmQzsQ39siLDWZnu1tWSpc0/DNgZPV8DrqyqF4nIxSOu2RC2zcTS0NDQME+4tPlLtRV2MnCaiLzdP78b8AYR2QfnWTY1bKuJZRRTWZXRrCRhLAY7KWoshTT7G2YZYaEXfWE3HXgZyxzZDyHWVWpckGNtJtdkLP0lWXpmLsWryao08yVNFoJpXUx0pPfA2Pu3XGpLwZPJQV8eVk79XH8puRbHnZWYRIH9VOk29JlKMdGkkXLF1kf8g8pDwIaaSb/MTqaZ6TSWjhI+lyRBadDr4i95uM4YaMp6yzJpLKr6DBF5N3Bz3MfxiChT8gOmOda2mlgaGhoa5gWX3Xjrb4WJyH6q+isROQA4299C3QGqev60x9xGE4t2TCX3+CrpKHsU2ExSVmAlK9Lf1Z9X2vxJgy17jCX6YZQ8xSyvsO7xCqPr4lNhM++xJAVMt7jsR8F1w3Sp7mMD/V1MjLqq0cGT1nEBRU8xI+Cxn+6Ffl2iO+SRjn0TzCDISfQXq6yyrhy4mTEKjJeRvG+aFxWDM4ev0Ron01HGJZ7sJaE02MyUMEGusEXiZOCuOIFecW9lfD/fJJQicmpFH+er6oOnY05DQ0PDckARdg22vleYqt7V3x8xrzHHMZZrAX9eqBfgP6ZnzsYhMpqpxOwhtLHYSdceg7FIv6+uzTQ1lgpYDClGiZ7nrGRg2JkyjzSdSqyr5N5gcd+7PEuI2WLPeywys6e7xELKSrZXPuizGbGYRKcx9HWRwEDU2OdPV9mjY1VyhpMmVey3LyWa7KVfmZBlxM07Kyo0meIBYYbNli5UZDoxMjaSJJMMZcZBb9LTVqxgGqMsP/BrpGEbxzIdTSyODj4AOMLrLYcBV1DVXn6xzWLcxPIUVf1YqYGIPH2K9jQ0NDQsBZbQK+wluNXZbYBn4E6QfCtwo2kPVJxYVPVN4zqoaTMv5EzFYiA5i7HZjKG7FONZfLr+WafNr01CKX0GkWO900z67Cb1/PIajtU+01YS/SaQC2OFaMW95LqLGKvZoReZEf8Sl4UIfXOVnQkjYzSGPCmiGHEplsNYTdS7yUr6Uk6RsRSZh6H9ZKaPiAkZHUFvenmV9JdI3whHIgxjfKKOczZSYDwJ07HKenb1+5gWlkG8j/D7qnpDEfk8gKr+QkT2nMVAVeK9iFwd+FuGJ4/hDZt6VsyGhoaGZcAcz7yfFtZEZBU/e4vIQUzdncGh1ivszcB/Ai9nxiePbRSCjmQq8T5/XpdqLOtJXVxfYiXDvsqf0UZzhdVcP2mfKyEmxOhzvaixRO0DKzH0l4BdkR6yR3YsbFJn6C7DcVxh+OIlbKbzPjLKzKh8N46GlabFDIxjkcsHg1l149unK+8wTnp5el12X1s2YV1t2vw8f5hFBkqxLUnzXv8FHcVKsxCX9XKF1eZlmBzLpLEAL8RlSb6ciDwLuDfwd7MYqHZi2aWqL52FAQ0NDQ3LCIWl8AoLUNWTROQM4La4qfyPVfWrsxhrnLvxAf7hO0TkL3GzXXzy2NQDaxoaGhqWArp0W2EA3wR+hf/vF5HDVPV70x5kHGOJA2rA6SwBMwms2ShEItE+cw220rZ0215GwslQB9EplMZ2V741tWq6HVdS78DeDTGwuo8J+lwvuBmvRPsKYesrbI/FK7TwPg3Mur7NeUBljLAtttK5osZCcAhqxKjzXVrBdpm4DpF2b6SBtxI65mewpycb6si6UqqVmjQs5vaV8bx4eqP1fBIbMOpG9Uv+Pht7er32seCepoWJtzZ7gZWJDQWB3qpbMco2CGW5tsJE5K+ApwI/we0sC+5lHD3tscZ5hR3hDdpbVX+XGbn3tI1paGhoWCYsGWN5DHANVf35rAeq1Vg+BdywomyhyAX5nMG4OpvVxHU7jPZdQkv67YfP+yxgchdkn6gx+sJu1o15XQ0mZabI9yJ5NF6PxURsIxf2rbq4rGMl2meSkRG96yKrfZtC8GTUrnNvjbvIF7OJO2zaJrmsE7YNUTkX8ZOBKttbAZjBroLgLr0HfRMsVtbrO25uOEKY7tOFsU2G00ulb6VtyQIlAcm/E3ESStNzIPu+zyili7J0E8v3cSdGzhzjNJYrAAcDlxCRGzD8nuwHXHLGtjU0NDRsWbiULls/jiU6kvg7wEdF5F2kWvnzpz3mOMZyB+DBwCHAvzKcWH4FPHkaBojIq3AJ0n6qqtfxZQcAbwQOB84B/kRVf1Hsx3A3tgIec6ayI9JTTI0l01ZiVpIziRVjObTR1C57THEhtGIsKQdi6C5+yR6zmR6LieMQfdma11bigMdd/j75gmVDxj/KPGBVI5YVxjFdizHKQl8hbct6f7/eYiBmSpOcxVgJKgs6h0kWjNV/MQhyVJvasknrIvQTTfbrwucqBpMouhubLMugWYXfQvHwr3kESC6HxnIpf/89f9vT32aGcRrLa0XkdcD9VfWkGdnwGuDFwH9FZU8CPqSqzxaRJ/nnT5zR+A0NDQ2TQ5djK0xVnz7vMcdqLKo6EJGHAzOZWFT14yJyeFZ8LHAr//i1wEcZM7EIVvBjX2MJbCSwjT0kYiyGjpK3j1lJ3ytsdN00sTrGS2w99wKLt5j9D2Hd8mDrAh2H13f6TPD4ithc7uWV5E3yJu4alnRMaGJtpVjn9ZSCp5hG42lgScaqWS2dI1vZ2wkq6VXWpHmxtB+TSWSsaVxOxbx/izVV2TDW5nSEie2K2+dSmaWZmN5eBY2l03Rm8+e/hBrL3FAr3n9ARB6P2576TSicYRzL5VX1R36MH4nI5axGInIccBzAJS6/74xMaWhoaLDRJhYbtRPLQ/398VHZwuNYVPVE4ESAA655Oc01ksBUrLiUwFRKHmCuPo13sViJxSCqE0ZuAOMYS2nskHZlh0+Qsm4c9BWzmfxwLkt/iSyLjFgPjWLDgGG8S+lAtbhukNVZMS7xezIQ17+lyYR+140U+cXYFjMuI1uCJyt9YyVdk4IfixoEG/pVpZT1xUPD8uvHlG3YK8xkF1b78HkY391Suv3RToG2B5ihLW4Uy5YrTERupqr/O65sGqiaWOZ5QIzHT0Tkip6tXBH46ZzHb2hoaBiL3rbz1saL6IeIWGWbRm124x3AI4Fb+qKPAi9T1bVpG+RxKvAg4Nn+/u1jrxDtMZUho4g0k4ypxB5jOzqNJirLGUvMcCgwFqPMiswPqDnitHR9jGCz1WcehR+zm8BmVmKPrKyPWH9Z8y9xxwhrXfvovc8TTZZWmcbvNXh3DRJPrj4rCUdUh0STZlS+kTZ/uNK3luXWXv6IPqN25YSO/T5KOkeJIUxcVrAhZUSeSRhso+QxlrcB460z2IzFDIdMKrNlZF9Z2ZQ9wQJ0ScR7Ebkp8AfAQZHrMbiwkZkkO6vdCnsp7v/jJf75A31Z6XTJKojIG3BC/YEici4u5cCzgTeJyMNw7nH32ew4DQ0NDdOGLsHEgnMt3hf3f3+pqPxXuAzHU0ftxHIjVb1e9PzDInLmNAxQ1fuPqLrtJP0Io2NUYs+vnKnsiFPq+3aWjhJYwKqhydTmCCsxDivWZJLra/scZH3EdgY2E7OYNb+g6TSZ+HrPKorMJWIeId7F0lHyspg1dWzRf1aDyMunyyMWs5KeE1HMcMKqN1xPVJfeuyf9ff28veb6SPZwFGr1ip57l8Fmip5chsxRxWYMW5OqfJykLjc6atd5a1XGqnRxMr6gdEDYiLE7TDFXGHPSWETkecDdgJ3At4GHqOovfd0JwMNwub8erarvy6/3pwB/TEReo6rfnbnBjEicYWBdRK4anojIVdii57I0NDQ0zAuqUnXbJD4AXEdVjwa+AZwAICLXBu4HHAXcEXiJP8hrFPYSkRNF5P0i8uFw26xxFmoZy98CHxGR7+DWFlcGHjILgxoaGhqWAfOKY1HV90dPT2O4fXUscIqqXgycLSLfAm4MfHpEV+HAxlcwY2JQ6xX2IRE5ErgGbmL5mn8xWwaCjtwCMxNNdmlf4vQtg+R6GG7R7FjZ5dv0+8rTvsRYrcx6l28jrRtksravvM+kLwmp7sO21/CHMbBeR352fWxCuDTbEov73SP+/pYE+myc0jZZ6lrcd1OVrI/4tx/KujQxkQnaqeT9bZXQf5LPU9I29haV5Z5suSIzvs5AyQXZ2u7Luxy7HZddWzrzvjtRkmjbqsL2pFkx0WSpr2Flr9mMxHs0Cwou40AROT16fqIPl5gUD8XFE4LL43haVHeuLxuFuR3YWMtYAH4Pl7trD+B6IoKq/lf5koaGhobtCWUi8f48VT1mVKWIfBC4glH1FFV9u2/zFFwyi5AFxRq8JMTO7cDGWnfj1wFXBb7AkEIpaX6vhUJkNFPZEYv3GVOJWUC4fkeS5mWQlNmMpR9YGTApy+j6jsbZaB9WXwHh7PfkUK/ADOLva8hUb6TBJ1+VGnVrkTdjEOS7QNToRzlMvyO9vgbrKQOJ3+fgsBGvWPOgyUG07Tx0T/b3kQ3Duuh15G7Dxmo+D0iM+7LSvJgxkBXMoCYYMqkuCPQ1TCcpqxX0S3Xde2mwxUJfRTHeTFoZxpGRNkwH0xPvVfV2xZFEHoRL1ntbHWZoPRc4NGp2CPDDQjcP8vczP7CxlrEcA1w7ekENDQ0Nuz3m8Y8oInfE5Ur8Q1X9bVR1KnCyiDwfuBJwJPCZUf3MM9C9dmL5Mo6m/WiGtmwa+V58YBkll+JYTxkGQ/YDJANT2SHDtIp5mpdppnbZUamthTFLEcAW4wm6SxIgGdpp9LXwrz9EwiZaUKaZxMGUK1nqmLh9cBceFHSU2K49MjdjK91LqrGkQZN2gCS964Z6yrCo884NGkvJpdg86IvRiMcpsJmaQMSSLrJR12KzusRcptBXsa6UhNJi0gEzOugL5hbH8mJgL1zORoDTVPURqnqWiLwJ+Apui+x4VR35xyEilwQeBxymqscF3VxV3zltg2snlgOBr4jIZ0j35u4+bYMaGhoalgGqsD6Hg75U9WqFumcBz6rs6tXAGbgofHBbaW8GFjaxPG3aA08b7qCvVCOxUrTkTCXRX4qMZdBrH9hCMbXLtJdI2EcVx3blnir9ZJGRzZF9lifaSlaWB1j6RqGyh1hjWQ1HEgdbS15hif6SshgzeaWRmDK8I7UeY1aal9xLK+6rdAiYHbiYXVBiOBYDsaSGCdmI2UdFX2Y6lUkZR3GcVMuKxylrOYaOElBKRjklLJk4cFVVva+I3B9AVS8S8zS0zaPW3fhjpXoR+bSq3nQ6JjU0NDQsB5YkpUvAThG5BH5Z4YPeZxI2Mom7cQl7T6mfTWG1t0/fZxk5U1kxUrRYLKbMWILGUufJFcbJE0LWwoqXibHaYx4Rm+m0FW9D1NbSg/KytaQusIt+sschI4r1kEF2Xd/DbkVG92XpL1bZIGMx8aoyZzHpMccOJe8u83+k1mNqEuYxoV5T0jJMD7PwvPL1bHSc1Csu8wZLtI/0e5bGpdTX5WNOVDchlKlE1c8TTwXeCxwqIicBN8MdPT91TGtiWS5C2NDQ0DAFLNMfn6p+QEQ+B9wEN78/RlXPm8VY05pYFg5TY+m0ln4kfYmd7LCi8Y0klDmbiZmExVh62khBF7FgaSsmQtp8o8+gmQRWYx1RnCDXESzU7mUHr7B192CQvF9ugD262JNIY9FUF7ESVIrBcEoHfZnR6KGPJLbFF+Vt6CemtLzJLOZhxqP0HkR9FTWGft1EHmaVdRN7g+WJIzcxTk2fdqZNq26K0KXbCgMXmb+K+++/pQ90/+9pDzKtiWXp3t2GhoaGzUIHy/PXJyKvAo4GzmK4DFRgMROLiOwDXKSqAxG5OnBN4D3RQV8PnLZhDQ0NDVsdS+YVdhNVvfY8BqplLB8HbiEilwE+BJwO3Bd4AICqfnk25tVDZLhVlLugxsJuSCZZ2vZK0rx07XwSymhroyszxHvLzbh0Vv0Ov/ApBjqOEe1z5K7CMBTT142ULsPrrP0LdxcHQQbhP7ggx1tboV2c8iKUhS3L9Wi110v3Ep1U2fs8C8I+gGpaFp/VUhc8abjUGmeuDBNTGu7D1t7WBC641SldarYqi27EhXEwdpGK4xS2o8Zcu+GULsXz7Uu+1ZuHsnRbYZ8WkWur6ldmPVDtxCKq+lt/ouOLVPW5IvL5WRrW0NDQsKWhjHAT3LJ4LW5y+THOzVgA9ee8TBXVE4s/N/kBuNPKJrl2bljJGMdQvB+mYclTtNjsJBb0U6YSp3QpCfuWeF+T3iX0lZ81X3t9DIvhDN2N/VIv0ZbDa4s/2l1sBuvSd2deze5hyHaCiJ+6FI92Re4yekRlVmBkboPdph9s10vpYgrBBm0w2MWwzmqf9m+6KVsouQtP6Eq8UUZRU2fVF9O9lFK0jPkvLwZNTlnIX7KtsFfhZIsvMdOw0frJ4a9xp5a9zeenuQrwkZlZ1dDQ0LDlIUsl3gPfU9VT5zHQJJH3H/MiPqr6HeDRszRsUlgHfa12q+A4cDFlHmYwZMw8MqayZ5xS30/6e/q6JNW9MfYkWDfWW1ZfVqBjqa+gZVhsxrZ1D99/GGfYVx5sGafbD4+TQ7n8EjX0kQSnZkGTpo5ipXQxyiTT1upTuvRfff4xJC7FFfpDtV6Rv/W1TGSjGktNHRt3Da7TYvrft82nz+/XFw8B2yyWi7F8TUROBt5BmvNxYV5hNwVeCewLHCYi1wMerqp/OW2DGhoaGpYCyxfHcgnchPJHUdni3I2BfwPugMv/j6qeKSK3nLYxm4EQpWnJgiBLaVtSdmIFTaZMJfaY2jNjPckhYIVU+jVYMbzDyiwoTtuSftnjvgIDsxjRTp8uf88JfytDJtI/NCxJjhk0EoNlhASVA+szywMkx6R0CWXrVfqL9K4bJBqO368PXlTR6+7pLrFTmNF+WNkvKgUb1rl+1Y3Tq7MMLGot8eo/ZwblLubNfmbtFTbrrqcNVX3IvMaqTlalqt/PiuoODGloaGjYtpDK2+IhIlcXkQ+JyJf986NF5O9mMVYtY/m+iPwBoCKyJ05f+eosDNo4dLiiz1b2JR3FTunS9/zK9ZSkL+MQsDwhZozQp5Wmftj3aKYz7qjiPDYlZjghfqU7zEv664Od0UFfwUtr0Nkc6ygr3lYfl5IktJTkHqKjgjXoL32vsJC3I64L7+pK1saVhTQs9MoCi0kOFOvYTMpEYliHhnUHfFnJIUc87bWX9EGywq/RWGrqKDOD2bCG6deNTTS5VbCFTTPwctyxxC8DUNUves3lmXEjEXlhRV+/UtWRk1LtxPII4N9xeWbOBd4PHF95bUNDQ8P2gwLL5RV2SVX9THYEixVPcCzwD2P6ehKwuYnFZ8B8QE3bRUEYnWAyXuEPGUhY1fbrrNiWXE9x7XIPM6svw5MrHGm8QSd4iwXFyPtdlz7L6L5PxjHEJawnOkrqKbaqceR9GpXvxk5Z3ErUfiXz1rISTebeYVZdDDHYTO4xtm61N5bSZp2kdWpSEIsS9Ytq6mYRL1LgX1OxZ2LWVINxEf6laPyV6U4ESxbHcp4/g0UBROTe2MfNv0BVX1vqyGdhGYkqjWWee3MNDQ0NSwOtvG0NHI/bBrumiPwAF5/4yLyRqv7buI7GtandCqvam1s0cm1l6BXWjy+x4lKs2JaS51fOVFKmMzqGpoOxeLK8tXKMjY2RtK9Y5+jHPURM2LOXOJV+p4uEdPsRyxj2bx3OFfSQ6GhiSRlO8rlk0fgxP8+1lXGR9zmjs7zCrLpgl3XCcPe2xbnFJl3/b3CxXLX6N74Ss4iWn7kOvcEYl5nGqpSwRO7GPv7wdj4ecUVVLyy1F5EjgL8CDieaK1T17uPGqp1YavfmGhoaGnYb1B6RtBUgIv8EPFdVf+mfXwb4m4II/z+4+MV3MGEKmNqJpXZvrqGhoWH3wNba5qrBnVT1yeGJqv5CRO7MaBH+d6pa4yHWQ+3EcjxwIsO9ubPZYmK+iPYCIi0xvkuEKOl2WVy2KkZ7w6U43wKLxfs9u1MlhzbWpHepEb1q08SEvhIxP9tpSJJd+tcWb8cF1+B10i1EVxYEev8+JOJ92O7qp20J22Px57KL1aR9ehpn2HIb7QhhIQ+UjMusHRcraeWwcnTVxutGb51tOHnjZuzZLMb1XUx/7+sGM/inLiW03BRk2bzCVkVkL1W9GEBELgHsVWj/7yLyVJwXcJwC5nPjBho7sYjIKvBIVa3em2toaGjYLbBcjOX1wIdE5NU4yx+KS6U/CtfFZUO+DemJk7cZN9DYiUVV10Xk9/zj34xrv0jkAvCKwUBWi2zGr8qJxfs0aNJyKQ5leybifXBrjeyrWNzsoL/KnuT6GCtdmpSoMLAKUwFd9TbE6WFCwGI/2WVgKIHhxO9zd13EloLrsZlMsmM7owX6rm0cPGoI+j3xPgkQTWGlzTfrDZF20rQtdXXTd1OuxYbPoi+J6tEPYIPZjaI0+JagP7puZmfdJwbMfohpwZ+j9SXgtriP6xmq+r7CJfcArqKqOycdq3Yr7PMicirwZqCbXGaRFbOhoaFhKaAslVcYgKq+B3hPZfMzgf2Bn046Tu3EcgDwc1IKpMwgK2aAiNwRF+2/CrxCVZ9dbI+9Pw9ZQB1pmzh1yJ7doV59FmMfDJYylVhPCQu1PZPgxHpY7MS6ftX3v25EaoU+dibBhr4usJl4NS/91xgeB61lLbanY3+eNRg2pOzCayu+bC1yRR627wc19hiOUWeh01hKdcYfg+WCHJCkzc8vHBO3V8UIZrECrmU6k9q8SSzUrXlKWDKvsJsALwKuBeyJ+2/9jaruN+KSy+NS7X+WVGOZjrvxPLNiQqfr/Adwe1wKmc+KyKnzOKu5oaGhoRpzmFhE5Bm4NCsDHHt4sKr+0NedgDvVdx149JitrRcD98PtPB0D/D/gaoX2T92ozbXnsVguZxcAp6vq2zc6eAE3Br7lA3oQkVNwb2xhYtEeq+juLc+vLLmke5xqNPG1wwSVoz2/dhCzH/HtY8ZSvwxb0/6G9A7pc5bgMbUj6joEOA7MJagv69pHukh2EJfrP6z6gz7S99YKLCZ5n739awxZyTDQcbXXPmcj8WeQB0yNS2lTYjElz6/SlnwvGWVl3VRQwzislzXzYMZS3ewo2DR0m2lhTozlear69wAi8mhcHq9HiMi1cRPFUcCVgA+KyNVVdWRuJlX9lois+javFpFPFdp+bKMG1+7O7A1cH/imvx2N2x57mIj820YHL+BgIE7Tf64vSyAix4nI6SJy+m/OX8urGxoaGmYLlbrbZoZQ/VX0dB+Gs/WxwCmqerGqng18C7coH4Xf+uz0XxCR54rIY31/CUTkneNsGtemVmO5GnAbVd3lO30pzrf59sCXKvuYBKaDSa9A9URcfA2HXWc/zVO4hBWxmWjS8BgLSNuree/GcfeBqVh6SsxSdnQeVqN1kQ7GO7DDWAesGsvsoHWsZQk3XXt3P+g0lqivwE4Mb60wTqy/rGXxKDE76bzvIua1lsWqYGgs4XOx0jpYafOHdZUsJfcwK3iMJddu8M+hWpOZpG4aWKSGMfPXNkvBKsIcAyRF5Fm4rasLgFv74oOB06Jm5gI8wgNxusqjgMcChwL3Mtrd3DtrjTQHuHbJ3tqJ5WDczHaBf74PcCXvinzx6Ms2jHNxLzrgEOCHMxinoaGhYeOon1gOFJHTo+cn+oUxACLyQeAKxnVPUdW3q+pTgKd4TeVROP2jagHeVah+1z+8CHh6wdZjC3UBRRfk2onluTj69FHci7kl8E8+YPKDlX1Mgs8CR/okaD/A7SP+ae3FeWr81STGIWUxiQdUdlCYa5/FxBhJJQNTidfRQQ9ZjRmL+BW7kYQxlO0K62bzaGLXZg/6K/0YPe+p6Os36GJPClHsUVXuwZW+X54ZaXgNKf/p2U9gUn2EfncZ11UxFTOYe8bLyY3GnGx63Bn2PY/+C5ilF5rreLo9T/AVO09VjxlVqaq3q+znZOBduImlagHuY1dKE87R2fMNaysBtV5hrxSRd+P27wR4cvBKwGU9nipUdZeIPAp4H+4f6lWqeta0x2loaGjYFObgPCAiR6rqN/3TuwNf849PBU4WkefjxPsjgc8YXdx19lamqPUKE1y05lVU9R9F5DARubGqWi9iKlDVdwPvnlX/DQ0NDZuB6Ny8wp4tItfATWPfxZ3oi6qeJSJvwnnL7gKOtzzCoi0wROQKOIKgwGdV9cezMLh2K+wluBd1G+AfgQuBtwI3moVRG0XuXpynb7HarBhbW+l2VxogGQdBBhffle75cKsmF+pdu+AaXHjbu92VvpQctsCK1wNrGgI9vfuwxjbg69LnMAyaTAT68DoK21glMb6YmqUY3NhP2zJN1J0WaddvfvDCFtpU+p9Nt1CRAHN3wxwi71XVEthD3bOAZ9X0IyJ/jnNV/jDu43uRiPyjqr5qKoZGqJ1Yfl9Vbygin4cu3fKe0zamoaGhYamwRJH3ONniBqr6cwARuSzwKWBhE8uaj4ZXb9BBzGV3sR6CGskH+yauZm2s9C3WyYajnrs+giuu9MpioX0c00jaRMMMOieEupV7aBdcilN3Yx/UqH12tmasQYtMpXt/DaYyw1/cLPve9lhmNjMvN+IJsEwpXXBif5yZ/kLSeMGpoXZieSHwNuBy3p/63ow+HKahoaFh98ByTSw/AP5PRN6Os/xY4DMi8jgAVX3+tAaq9Qo7SUTOYJhu+Y9V9avTMmLayFe0K2ZSyVQ7AVuTyd2ME00iO1M+Doas1UNGIb5u0LlP1zGW7gz6AqkcpmOZ/y+jxIKmgZIL8mYxa/1l5u62DdODLj6lzIT4tr8FhHRcl7Iai8gxwFOAK+PmCgE0d0+2UPzXE5EDoqc/Bd4Q16nq+eMGaGhoaNi2WCLGoqpdUKSIrAD7ZulicpyE02W+xJTPvD8D99YJcBjwC/94f+B7wBGTDDZrrGavPU84GcPap7e9wtzjHV0KmLiP0MZrLBJrLNPzZKplKpPACpCcF9ZnvB4fzNBTR2fsBbRke/a7PZbp8xKRk3Guyuu4//ZLi8jzVfV5Iy75maqWUruMRPEfS1WPUNWr4AIV76aqB6rqZXEBN+2Qr4aGhoblwbU9Q/ljXIzgYbj8YaPwVBF5hYjcX0TuGW41A9UKADdS1UeEJ6r6Hn9GwJaElVhyVN2Koack7XteYX3Pr6HGMn1mEWNQyUbXNRwjrMn99O0Z/XrzA7KmO25TIDaMGa6wZ756LyVtXRS2oEkF7BCRHbiJ5cWquialcyTgIcA1gR2kZ96PJRW1E8t5IvJ3wOt9x3+GO1GyoaGhYffE8on3LwPOwR05/HERuTJQ0liup6rX3chAtRPL/XFJz96Gm1g+7su2NCxtpasrfCMs5rKS3bvHqbYSe4XNgr2sjT6/J0FgNiEtf6yn5Kn6Y73D0j42q4eU9I4S8xjMINo+RkkrmbWOMvNo7WVmJcuGJXo/VPWFuNCRgO+KyK1HtQdOE5Frb+Tk3lp34/OBx0zaeUNDQ8N2hbAcE62I/Jmqvj7EqxgYFb9yc+BBInI27sz7qbkbP01Vn7bZNvOAMJqF5NH2aV2Zy64UFper2Yp7mp5gMULur5yJjL2OVGtxj/F9+OdGV+sG2wrMxaqbNbso9V9kRItkHrP8w5m5ljHj/gtYOp1mCSYWhqdEmvEqBdxxowOOYyx/LiKlPTjBnZXytI0a0NDQ0LCUmF92401BVV/m70uHe1nXfXd8KxvjJpaXM36We/lGB29oaGhYaizBxCIiLyzVq+qjpz1mcWKZdIZbBhQF/aLYP7rP2Qj1w1Pfw6mS69lZ9uP7CFthQ+zs+ghbW8MXFraO4i2kdb8NtW5sR61nW0Gx+7HZviDWW1tsk6C07VUS48e5Ls9UyC/9KW3j7a7Zv7b5vbgl8Qo7w9/fDHdW/Rv98/tEdVPFxhJZNTQ0NDQsBWNR1dcCiMiDgVur6pp//p/A+2cxZptYpohpMJfAVAJLgT5TCUxkFDqB3n/r17VfF0R7y904YTHZa7KZyOjXXRL7a1iQdd2sUFrobtQ9eSqL540ym0Wynhmyhi3DEpSlmFgiXAknbYQcj/v6sqmjTSwNDQ0NG8QyiPcRng18XkQ+4p//ITNyvKo98/7qwEuBy6vqdUTkaODuqvrMWRi1bBh0br1xev7JVteDzDU4dinOmUq8YLOSSa5nRWuJjuLudxIOA4sZS59JBPYy6O5XjPZiXDeZW3KukaSsSUa2tTSSkm6yWc2kuBAf8ydT/BNalHvyZmzeJBb2fkwTy2InoKqvFpH3AL/vi540qzPva//9Xg6cgD9CQ1W/iHMzbmhoaNhtIVp32ypQ1R+r6tv9bSaTCtRvhV1SVT8jkqz2do1qvJURVtI76KdHGdYNOcFQi3DYIXF7f/hXlzolTmjpGcGELCa0jz2/cqayM2Ez4VjkKG1LtmJf076OElb9O6Njhdc02Nz37srZiesjbW+yB4P9hD4tT66NBjyW+rLtWmRgZalug0GXC9RTSn+c09BDpEATS3Uz9w5TttgB7VsHkyShvCrDM+/vDfxoZlY1NDQ0bHEI7aTPUaidWI4HTgSuKSI/AM7GZTjeMlCGK+EdkrKReJUdGEe+End1nhnEq0b/sPOmivrN09HHz4dpWIZlexVSvqwXPL1ypmJpJmuFNCwDg7GE172mFjvpaxhrukdyXf44vj4e04pdsXWRtK80libXWMrMbxI2Emsulv6yYY+vTS6WiyxgXN8btGuh6VSs3ELTwixZxRba5qqBiNwcONLrLQfhTpE8e9rj1Cah/A5wOxHZB1hR1QunbUhDQ0PDsmEr6SfjICJPBY4BrgG8GnfOyutxgZNTxbgklGY2zKC1qOqorJgLRbfylqCZ9OsCq4lXyt0qW/or/JXu+fCbtBISOfpvV5zWfphKP+rfL502G+8SL+4s767c9hhruprUxSxjzestoQ3AzsBUuqj8mOFI0t5iM4OExaReZ0n7rH+LlZS0kkn1mom35jXUGX127Ut1Vp+b30jZKLPZMCOatc5T1FNGX1bUWmaJJZpYgHsANwA+B6CqPxSRSRNTVmEcYwmDXgO4ERDOP74b7kyWhoaGht0TuoWCNeuwU1U1nBrpd6BmgqpcYSLyfuCGYQtMRJ4GvHlWRjU0NDQsBZaLsbxJRF4G7C8ifwE8lBklEa4V7w8DdkbPdwKHT92aTSJ3Je4C9yLRPNRZ2yphe2iHWdZPjxISUwbX4B3JtppruCMZZpVxKJ21Yi2OLLfhvC7GTr9tFbYA4+0ya0tr2K6/fdXVGe7DA8MRIE/hUrt9N8j6T8cZvwU2KAj041yLi/8bk25zaXZfic3u4xe3kMZdPO+ttmmgxCKm7CSwTBqLqv6LiNwedxzxNYB/UNUPzGKs2onldcBnRORtuK/aPYD/moVBDQ0NDUuDOU4sIvJ44HnAQap6ni87AXgYLtTu0ar6vlIffiKZyWQSo9Yr7Fk+FcAtfNFDVPXzszNrc+inAIlW2ZKvwIdtVzLRH2DVM4jO3dgIkOy+XBIHQxrsx7sUh+DJODV+cEvO75NxuuflVXovEDER1fO6fkqX4FoMQ4YTymJhP2c468Y4yXufuT9bjhNWUGOPgVS6MAd2Ygnuk7bfaBLKkkBvrnYLrEZq6maFWQj6GxXjFyXUG5gXYxGRQ4HbA9+Lyq6Ny4ByFC6Z5AdF5Oqq2o/+du3vCTwHuBzDMBxV1f2mbW+Ve5KIHAacB7zN337uyxoaGhp2T+gEt83jBcATst6OBU5R1Yt9LMq3gBsX+nguLsfjpVV1P1W91CwmFajfCnsXwxd0CeAI4Ou4mXJLQJEoKM+vxjt2Eq3+fZs8UBJg1S8/ktW/BA3Dt0sCGX2/YdkSfeSh/yTFfQi2NFZcwVV5mOq+38ZiKlaq+2EwY9+leED6HqV17vHOiJUMXYT7feXaSsxOOjZjBmAW0sOoUZexH0tjMcsqUrnEb7Olt/TYSCmgcmzg4oTspeK6Yl+TMpsZajmbGrtYtzhmI0zkFXagiJwePT9RVU+sGkfk7sAPVPXMLK3WwcBp0fNzfdko/ERVv1pr8GZQuxV23fi5iNwQePhMLGpoaGhYFtTPXeep6jGjKkXkg8AVjKqnAE8G/si6bEKLTheRNwL/A1zcXaD634VrNoQNnceiqp8TkRttZmARuQ/uLIBrATdW1dOjuokEqYC+V1g/xUu+Ul+NlhzW6j8EMwatJQ6eHH6EGXNhGDy5kpT5PowEmMGzbKixRK8rBGJqamf8emKEejNwMdNd1rXfV6yxDIMgR2ssVt3A0HACI9gVPNOs5JiGNpNfP2lZiYnU6ilh8VsKnrTaF3UUC/OqK3moRWW5/VMJRNxgEORG2cksgyen1beq3s7sX+S6uB2iwFYOAT4nIjfGMZRDo+aHAD8sDLMf8FvSSUqBxUwsWQT+CnBD4GebHPvLwD2Bl2VjTSRINTQ0NCwE09NPRg+h+iWc2A6AiJwDHKOq54nIqcDJIvJ83H/lkcBnCn09ZLbWDlHLWOKw/104zeWtmxk47PVle4YQCVLA2SISBKlPF/sj2p/32sqqX/fHq/LVnv4yXBmvdEke47fFe26FLqIv0nqnrQx6lV2Mi6GxWKhJjW8llRwY2kdnX4GBWClXLO0jXJtrM0l7g+l1ekqh/zTdi2R1ozUT66AvK1ZlYLCS8LbaLKZXVJnKxXhew1SS9hPoNWO1nOnXlZjXxKysBiXtYqyWMz/dZZFxLKp6loi8CfgK7o/q+NICXET2xu0EHQXsHfXz0GnbVjuxfEVVk0h7v5U1i+j7akFKRI4DjgO4zBX3tpo0NDQ0zAzzTumiqodnz58FPKvy8tcBXwPuAPwj8ABgJmJ+7cRyAv1JxCpLUBKkVPXtoy4zysx1gfeqOBHg0Ovs17UZ5R3myoLXlWsTJ4QMeosV2xIO0topQx1hz+DJ1SWcHCIwjj0lXv3Xp8ZP0vNn2kqqsfRjQnK2YDEQ0zPL8BTLdRpbYwmaSd/7LG6faytJev7MK2xg1hXYjMFiSvErajCRTneJ2td5fvk2NaxmVLvQ0zSZwTTZSVWf/cqkr55eUzlmCUVmMwc6sUDGsgFcTVXvIyLHquprReRkoEq/nhTjshvfCbgzcLCIvDCq2o+KEyRHCVJjMKkg1dDQ0DB/6HKldMEfLQ/8UkSuA/yYGaXmGsdYfgicDtwdOCMqvxB47CwMwmVQrhakhpAoxf2Kvw/sJNJRuqN/+y+98xCLvyyZtrIaLZFC+xBlvxrrIoGpGKumoL+sG1/KtU4zGJblqfEtLy/rcK48zxcYucKsGBdDF8nZibMnZRkJmxkYnl+91Pgxk6pnJeMYS6muJleYracU2tWykgmYQGk1P4659Oo3yk42cW1pi2hiRuR/Q8Vtp0XFsyzXxHKiiFwG+Hvc/+y+wD/MYqBx2Y3PxLm5naSqUz3jXkTuAbwIOAh4l4h8QVXvMKkg1dDQ0LAICMvFWFT1Ff7hx4CrzHKscVthb1LVPwE+H3L4x1DVozc6sKq+DZcexqqbRJBqaGhoWAy2UN6yURh1YGPALA5sHLcV9hh/f9dpDzxtxO7GwyBD78Ir8TZRmuYlFu87t1wZkrOVLtItGii0z1PVR7sfq/l1EQZdoGS/cs0Utt3jnd0Jj6NT17trR7v1DscxUq4Yp1HmW2FW2pZiX9YJnaSBknG/VoDkrnybzHhv1NgeGwZBUlVnbW3lgZHJNln2n2K7K/fbS3aftjfKCnWTpqeX0V/LYiqYmaRcKaSwL7kMjw1KnNe2mC7NQV8zOSWyhHFbYT/yD/9SVZ8Y14nIc4An9q9qaGho2D2wDBNLOLBxnqh1N749/UnkTkbZQjEU71MRf2cUILjSMRX3jViNhW2r01y8j5aB6/kZ9onobzgCeKyZ68X0NayZArrFTvqr+J5LceFwrlhwt4MmUwZhps036gIbWRv0Gc6uQbhuYwL9uISTeYBkWpe2iWHrxhXuxsZKf+LVf09wH82MLNS69VbZUqjf8Dhj7SgxlFKfJUZUtmcq2Po7YR1E5CrAvwM3wVn+aeCxqvqdaY9VTJsvIo8UkS8B1xCRL0a3s4EvTtuYhoaGhmWCaN1ti+Bk4E3AFXEet28G3jCLgcYxlpOB9wD/DDwpKr9QVc+fhUEbhaoYGkuqtYChi0TY09+vJ0udoLuEgawrd/lxrIC8yb5VeQLJuKxjBvRZw04jbYuVgr50OJelo5QYS85UdiV1fa2kn5ql7+oc2Myu4nWj2Un8uBQEaad7MQIrM90lZTCbDIyMnvcYzqRMZ1btPcqax+i+JtWRNpyOZY7HEXdQlkK8jyCq+rro+etF5FGzGGicxnIBcAFwfwARuRwux8y+IrKvqn5vFkY1NDQ0LAO2EBupwUdE5EnAKbhp8b64UI8DAKZJFmqzG98NCAGLPwWujMsxs4UO+hqunFeywMXY8ytoLCHQMU7QaGNX0oeVZp9O0+gvm9aixyuFb2EeqJcctpUlezR1ETM5pFFXcThXwlgyb60Q+Bj3v0v7mklgHmuJd1fQXfqsJLQ3E01mLGPXoM90TFZSYDglL6/UUyxrZ2gMXfsC04G6wMWSJ5d5XZVeU1lnjDdJsOV4vSZ3o9voOFvDK0xYDvE+wn39fX6W1kNx7/jUYltqxftn4gSfD6rqDUTk1ngW09DQ0LBbQnWptsJU9Yh5jVU7sayp6s9FZEVEVlT1I97deAtBhnv+A/+yiq4JDntGMSvj2YuNEBOzZ7Rg3Wl831YLG8F52vuYZQzTsIQ4kH7szc7EWys9eMtiOHnKeyjrKFY6mZyp7BrENhgeZhv1/CJlKlbMShJ7M5ggjmVYNUJjye5j1HhF1caeZKzH8r4qxb9MqnMUyyZ9HeOST05QZ/5EOtpYqDPHmf2f/pJthc0Ntf+kvxSRfYGPAyeJyE+pSELZ0NDQsK3RJhYTtRPLscDvcIknHwBcGpfPf8tAiVavkuoHsWdWeLxGH4G9pHqFa2/GqnhYyStX+weYmRHww3HS9knqetJ4EZPNGMzAOmwr11YsdmKlzR96ZkV1gzwJZTxOvyz3+IoZzrBsJXkel5ViVtRgOKWDvqzrLK2k366go1hahsl+Rus1I5+PgMUyisxm1PWjxiyxsrzOZBsjrh039oQeYGIJYzV9bRKNsdiomlhU9TfR09fOyJaGhoaG5YFipyjfwhCRuwO39E8/pqrvmMU445JQXoi9jhFAVXW/WRjV0NDQsAxYJsYiIv+MO+b9JF/0aBH5A1U9YdpjjYtjmXvyso1CkeG2TqDKBfF+h7+3tsSKiMT+MOV220PRPkOwJRbs1wv7G5YwP+zLuzMbAnpeF5dZ4n1eZwUpxkJ4LtCnZ6hk22TJFtro7S77lMjxwr4VDLnut8mCYB+3qzolMtlB6Yv9vW0ua8el29rqb3vViur5lpa5TVTacjP6r2ozsVBfqEv60NF1EyafDFtsphg/qWvxtIMll8grDLgLcH1Vd5StiLwW+DzuNOCposJvqqGhoaHBwpKldAHYP3p86VkNsjH/2i2KXkp8DcL76E92R/Q4pH5ZNwIq7fECGwkspv92WkGTQezPXYxdn/2ywBby5JJWnesjZTZJ+64uFfjjx1b/XeJIg2WUAiSts+tzoT5+bNWtZ3XrcYBkRSp9m82MT9+S1o8W3M3VfM3KflI34MJ11axkQuYhBVffkl1VrtfRaj9nIxMnnkx8ygt104SybF5h/4Q7W+sjuC/0LZkBW4FtNrE0NDQ0zAvCfGJlpgERWcFNuTcBboQz/4mq+uNZjLdtJhbVKEV7xQZfnvbFdTLZ29FzEbbYiXmY1+hEmJbGElb6+Xn1UE5/b2ssqbZi1aUHcKUMJ9ZM8jQvqTbTD5DM2YgVBFkKkCy5CFtl60ZAZa6tDAblvvr6RpnhFOuK7smFOg/Ljdh0LS65G1e4/E6cGr+oJ1nsYnxfZh+mZmK07+qssaebg0WWxCtMVQci8ihVfRPuvPuZomksDQ0NDRuBTnDbGviAiDxeRA4VkQPCbRYDbR/GwjBt/nqnrfgVtZUq3/Ick3VfFBVmX4p4L38lm5dXLbYx4WlDFmPJdZTYBksXyZnKwAxc7LOMjp0kZSm7sIIgh1pLv64U6GjV1aRtSbzCOt0lat/1Qa+vzrMstB+bviVlEvGieait9NlJzcq+yBYm1VFii7M+Es68wXGKnl9jAiPzPmxPMbtvE2M0lt721My2q3TZvMIe6u+Pj8qUKSafDNg2E0tDQ0PDvLHFPL6K2IpJKLc8lGg1nbGRHax37bq9fAn6QPQWWBuDYbkX9u2jRiEFzDA2ZHhZ5/lVOIbYQn50cFrn+ooP9bKSQ9boKGudl1ccx9LXPrp4FIP97MoZi6G/7DI8uCzPr9yTK2Yzw1iV8N6M9gCzyhJiUEhQWU5CWUjDUvC+KsejDMepSndS6+VV8NaatKx0AFnfK6zfQdIm1zwm1GTGpnKp6mvKM8ESMRYRuSTwOOAwVT1ORI4ErqGq75z2WE1jaWhoaNgI1G0D1ty2CF4N7AT+wD8/F3ckytSxbRgLscYSlklhBW1Nn/7D3rESsZnu6F+r+YrvyvL86peFFXuuw4zDwIgvCci9vWKbY2ZUYiy5JhN7gAWvOrP/QkR88Thhw1PM8gorxaqUI+lHl3XMxfD8KjOQPmMJ18kgrkv7SOryNlTqGhXaRCli3yqrTcE/LNNe+17flu1Wu0pWEsbsbBj060rXm6ezBczqaOJZ9+0hIk8D/gL4mS96sqq+29edADwMWAcerarvK3R1VVW9r4jcH0BVLxIxsuVOAdtoYmloaGiYL+YYx/ICVf2XZGyRawP3w53keyXggyJydVVdtzoAdorIJfDTvYhcFbh4FsZum4lF1dJYvJdXvMqWsConbQsds1mJ2ULXZeppFmNglFneXcWDvrL2+VHFcZtxjCVnKlYafCtaPvfygji+pJ/fK2cqCTsZWO3TdjErCX1YOkfw+Cp5gNleYVbcS4H9DCwWU4o9GXFPORq/hmVUazn59XFZv2jDUfzmoV4F9lPUSro+RzOjEqsx25UOCIuxvXKFHQucoqoXA2eLyLdwSSY/PaL904D3AoeKyEnAzYAHz8KwbTOxNDQ0NMwVSjlAM8WBInJ69PxEVT1xgtEeJSL/Dzgd+BtV/QVwMHBa1OZcX2abq/p+ETkDF30vwGNU9bwJbKhGm1gaGhoaNgBBJ9kKO09VjxnZl8gHgSsYVU8BXgo8AzeVPQP4V1xMikVKRxokIh8C/lVV3xWVnaiqx1W9ggmwbSYWRYburmGHxQqC7ER7d5+ksjcE/eCOPPBqYpy0cq27Dynyh31ZIv+wXT/ZZY7kRMjs+zMwtraSUyKzVCuTnlOfCvqjT33M07DsMra2rO0xy6VYs+2xeGtrPeur5FoM0S8rE97dk1TQ10Rw72+F9UT7kityjArngOpz7UvbV5NunVXUjXMO6Op6QY1GG9M5wBfUbl/lKV0GRl2MnltzdMG0t64G03H5UtXb1bQTkZcDwT34XODQqPoQ4IeFy48AnigiN1LVp/uykZPdZtDcjRsaGho2grAVVnPbBETkitHTewBf9o9PBe4nInuJyBHAkcBnCl39ErgtcHkReYeIXHpzlo3GtmEsYAjNfmmUBPyF9C4Gm+mE5uiLsBL6MFhnLuQPEsYy2ovPTDGTYVOMJQua3JW4G6dMJTmT3hT0R6dT6dUVhHroM5VUoE+ZinkIWBDx48DKQd+uwEIGHSuJmFH4bIur+ej9zgV9a4XfHURl1E0q0BvPS+7GNS7FtX2ZKfJrmJRxqNeQlYwX8eM+zDbZH7N5GFgN05kB5uQV9lwRuT7uXTkHeDiAqp4lIm8CvoI7u+P4gkcYgKjqLuAvReTBwCeBy8zC4G01sTQ0NDTMFXOYWFT1gYW6ZwHPquzqP6PrXiMiXyLNGzY1bJuJRaFKY9nDp3fpMReGukucmiUc9NWtiOPNQ7/C79pEm84lxlKDhLEYrr4BZmqWjkF4t+MkdUpwRV5Jnrtx+rpLrq2YiSMLKVpMJmUkmsyZisVK1rPkknEftktxTdoWIxgyCc4LdQVXZOu5pSNk2oqY42T3VtkYDaSzwmIZeZ9jyjZ6OFmJlUh2H/cx8THEFisZRsjW9bFh6KLdjSeCqr4se34Gw8SUU8W2mVgaGhoa5gplqSaWeWJhE4uIPA+4Gy53zbeBh6jqL33dJGkKHFRG7vmvRSvwsLDvMRd3gWsSZTkInl6W/tJ1aekwGZuphRlsaXhYBayZh3+l78OacXBXzlxgVGqWlC0VNRYrPb2ho1hHBudMZWAeJ+zbWskrkwDJ0Z5fmnt3xQ5DNan0Y2aQayslD6gxfZWum4jNWP1OyH6KOkqRZfUZSDlpJX2U9JOB0abGs2yGaVeW5aCveWORXmEfAK6jqkcD38CfvZylKbgj8BIRGa92NzQ0NMwbqnW33QwLYyyq+v7o6WnAvf3jY5ksTYHrj2ilnWkre0TLmrVMh1mJP3RfthqVdfErhv4yvCzTYeK6CQ9sKKVysepKOsq6wTJKcSy5V51rn+onVqzKriytfVwXl+UaiaW/DLK09u66UGe8D5bjz0RpWPp9idVXYCelRJOWJ5ORhHJiNlO4riujX1byTCsmnBzHYrr2aeHYo5Bz3cnSRQz20/cYMwYqHUNc6msz0BHjNmyZOJaHAu/xjw8Gvh/VjUxTICLHicjpInL6xb+8aMYmNjQ0NMSoZCuNsUwXpTQFqvp23+YpOB/sk8JlRnvzk/G5dk4E2P+al9Nu1SvZajzxCnOrmMBc4ij70N707hoMS7pHfjnW02FGWF5iLxYb6eq6w7/6DCSgpKPEdoXXvdHjhM34EqPO9tbCv45+dH0eeZ9so+dMJakbHatSE12feICFdnH7nHkUtI+i/hA/Ng+syvqakM0UI+g3o7EUxum1TxibRSVDe6Msf09Kh3pZxxCb7MdiNlM+HGU3nDRqMNOJZVyaAhF5EHBX4Laq3Sc0aZqChoaGhsWgTSwmFrYVJiJ3BJ4I3F1VfxtVTZqmoKGhoWH+UIX19brbboZFxrG8GNgL+IA/xOw0VX3EBtIUAOCSUKbz5B4rjvbuMtyNOzfgJH2Le7Iabel04r4xBQeRP2ydrYw5jG3V3tED0jQqOfKtrTzFC9gi/Hp2XdzXLiNAshToaNV1or11jovlbpxtgVkpXUouxd1HlrgP1wRN9re2qlyLo8f29pAPeMzTxMR1tVtH+XaadV1l6pj8ddjttV+XXw/dG1sa20rpYjsO6Mi6UvBkcWurKgnljMT7WfS3TbBIr7CrFeomSVPQ0NDQMH80r7CR2FaR92GlPoq5uLpVX+ZI0K5YjO/E/+GXJbAYS0zsmIqRbt1iJ7tqXoSHxUpy5hJjzQpqLAjunaPCmMSRpXPq8yBIy7XYSmiZC/XQdzO2XIpNob4T4euCIHvn1Bssw0zb0rEGS9i3+srucztGtJ9YcM/bUGZGNcGW5eBM7dVZCSdNJtRLs1/LQArPLSeB4JFRcCCYGhpjMbGtJpaGhoaGuaJNLCa2zcSiOlxVD7WV/sq+c/kdZM8ZspM4DUt4gy72K/eVyC8yaDErxpdr0POfnAwWK8m1k1Ht8kO2yhqLwWYK7salIEjLtbhUZukiNS7F6eLUcCnurbJjNuPui67FUVlN2hZrnO66ykSTubayYTZjlRX6stmJVrXvjZeUaa8udw1OfiId6+nb0Ltu0gSV48o2iiDeN/SwbSaWhoaGhrmjMRYT22ZisbzCcnaSlOXeYQw1lpjFhGDLULYSr/BDmcFOVs04z3pYXmK5dgKjkkPWBzUmHmCGRpUzFes4YSuppBUEGTy98hQt8TgdA0lYScpUbD2F6IKUjZhBkJUaQ69Pq/0g8w5jBLvI6jbMZqy+ChpL8TCvcRpLziCS9pq2N20YzX4sVlIV8DguRX7eLg6KnLbY3iYWE9tmYmloaGiYL7R5hY3AtppYNFuFd1pL5PkVUrqENqnG4hlIgcWsJOle+iwmIHiATSMJZVdXwU6sdqX4kl2W/jLos5Ka44StpJJqjN0xnfjF5YvSgueXrafEWknGeswDuPoeYz02E5dZmknFMcflJIyF9kUtJ7qu0FfNOEUPsNr2A+3VmelrJolHsSTKmhT5kT3FNtOAgurmtNTtim01sTQ0NDTMFY2xmNg2E4vqcFWdR9UnrCHTVmKPriErGc1iYnaSsxFLa5n4oK+CtmK1mfQArry9pafELCNnKmmsSqrhJOMM+u1DWflwLj9efABX3r6gp7j2JGXJx5IzlbFp87MyY+yqNPiRHTXtLY+pYGkxmt8os44AtlmW9sYuxbH0vc8KmkkEM1NBpuVYkfdS1FMGRlmfIU2VYTSvsJHYNhNLQ0NDw9zRxHsT22hikeHquIu0D6vt/odvspMKFmO1754X9JH82hw12orVtqijTMhmrJxfGz1O2Ep/n3uRpTEuqS6S1OWR9MlKt+9F1mMqBU3GXJ0bcSzWQV+9WBWjr9r093lfxeus+I/KcSbXWHRse/O1FnKeWayk5w1W6xVmRt4HZpNF4OePpwCddhr+bYJtNLE0NDQ0zBPaGMsItImloaGhYSNQmng/AttmYlFSoRhAu2DIOEDKb9uUhPrC9pjVftRz08gNIN8mK217JWWFbTI1rlsf9LfC8i0ws72RBr+U/j5sbQ2MQEfbpTjbAisI9RCLw6O3zuwtp0IQZMWW01jxPhPmzZQm1nWk7ceK9107Hdm+JMYX07wk72W+RUUf5pZWoc5I6VKVvNI8876ybINQQJt4b2LbTCwNDQ0Nc4Vq6o3W0GH7TCwapUEJwrHBIDqm4pdNsbBfw2LS4MkJGcsG0WMsBXZilSVBioyusxmLr7MCMTOmYqVvsdLfFwMXS8zASNFiifC9dPkWm8nbRu3NIEgrbUveviDGm+1KzMPoq/zeGGUlJwGLnYTFd20Syl5dX4wvMSkreNJ0T87djK0U+QWxP3ExnrLYrm0rzMT2mVgaGhoa5o3GWEyI5Yq7jBCRnwG/Ac5btC0GDqTZNQmaXZOh2TUZDgT2UdWDNtOJiLzX91WD81T1jpsZb5mwbSYWABE5XVWPWbQdOZpdk6HZNRmaXZNhq9q1ndA/JaqhoaGhoWETaBNLQ0NDQ8NUsd0mlhMXbcAINLsmQ7NrMjS7JsNWtWvbYFtpLA0NDQ0Ni8d2YywNDQ0NDQtGm1gaGhoaGqaKpZ1YRGRvEfmMiJwpImeJyNN9+QEi8gER+aa/v8wCbFsVkc+LyDu3ik3ejnNE5Esi8gUROX2r2CYi+4vIW0TkayLyVRG56aLtEpFr+Pcp3H4lIn+9Bex6rP++f1lE3uB/B1vhM3yMt+ksEflrXzZ3u0TkVSLyUxH5clQ20g4ROUFEviUiXxeRO8zavt0FSzuxABcDt1HV6wHXB+4oIjcBngR8SFWPBD7kn88bjwG+Gj3fCjYF3FpVrx/58W8F2/4deK+qXhO4Hu69W6hdqvp1/z5dH/g94LfA2xZpl4gcDDwaOEZVrwOsAvdbpE3erusAfwHcGPf53VVEjlyQXa8B8kBE0w4RuTbu/TvKX/MSEVmdg43bH6q69DfgksDngN8Hvg5c0ZdfEfj6nG05BPflvQ3wTl+2UJsi284BDszKFv1+7QecjXck2Sp2Zbb8EfC/i7YLOBj4PnAALh3TO71ti/4M7wO8Inr+98ATFmUXcDjw5XHfJeAE4ISo3fuAmy7qe7adbsvMWMKW0xeAnwIfUNX/Ay6vqj8C8PeXm7NZ/4b7UcVJhBZtU4AC7xeRM0TkuC1i21WAnwGv9tuHrxCRfbaAXTHuB7zBP16YXar6A+BfgO8BPwIuUNX3L9Imjy8DtxSRy4rIJYE7A4duAbsCRtkRJuqAc31Zwyax1BOLqq6r26o4BLixp+QLg4jcFfipqp6xSDsKuJmq3hC4E3C8iNxy0QbhVt43BF6qqjfA5Xtb5FZhAhHZE7g78OYtYMtlgGOBI4ArAfuIyJ8t1ipQ1a8CzwE+ALwXOBPYtVCj6mCdB97iL6aApZ5YAlT1l8BHcfukPxGRKwL4+5/O0ZSbAXcXkXOAU4DbiMjrF2xTB1X9ob//KU4vuPEWsO1c4FzPNgHegptoFm1XwJ2Az6nqT/zzRdp1O+BsVf2Zqq4B/w38wYJtAkBVX6mqN1TVWwLnA9/cCnZ5jLLjXByzCjgE+OGcbduWWNqJRUQOEpH9/eNL4H50XwNOBR7kmz0IePu8bFLVE1T1EFU9HLd98mFV/bNF2hQgIvuIyKXCY9ze/JcXbZuq/hj4vohcwxfdFvjKou2KcH+G22CwWLu+B9xERC4pIoJ7r766YJsAEJHL+fvDgHvi3rOF2+Uxyo5TgfuJyF4icgRwJPCZBdi3/bBokWejN+Bo4PPAF3F/kP/gyy+LE8+/6e8PWJB9t2Io3i/cJpyWcaa/nQU8ZQvZdn3gdP9Z/g9wmS1i1yWBnwOXjsoWahfwdNwC6svA64C9Fm2Tt+sTuAXBmcBtF/Ve4Sa0HwFrOEbysJIdwFOAb+ME/jvN+33brreW0qWhoaGhYapY2q2whoaGhoatiTaxNDQ0NDRMFW1iaWhoaGiYKtrE0tDQ0NAwVbSJpaGhoaFhqmgTS0NDQ0PDVNEmlt0IIvLrGfR5dxEJ2WL/2GeMnbSPj4rIMeNbJu2/LiJ3N+oOj1Omb3eIyJOjx5fwKf53isiBi7SrYfdGm1gaNgVVPVVVn+2f/jEw8cSyQTxAVU+d5QBLkkK9m1hU9SJ1ufNaWpKGhaJNLLshxOF5/mCmL4nIfX35rTwbCIduneRThyAid/ZlnxSRF8rwELMHi8iLReQPcMkan+dXzVeNmYiIHOhzqIWV9Ski8kUReSNwici2PxKRT4vI50TkzSKyb8Xr+T1xB759Gjg+Kl/1r/OzfqyH+/IVEXmJuEOp3iki7xaRe/u6c0TkH0Tkk8B9Rtnjx/yYzxT9vigX1aNF5Ct+vFMKNu8j7lCqz4rL6nysLz9cRD7hx/ucf18RkSuKyMf9e/tlEbmFiDwbCCzlpKoPv6FhHlh06H+7ze8G/Nrf3wuXiXYVuDwuB9UVcWloLsAl41sBPg3cHNgbl178CH/9Gximq3kw8GL/+DXAvaPxPoo7lArgQOAc//hxwKv846NxmXCP8W0+Duzj656IT9WTvY6uX//8i8Af+sfPw5/FARwH/J1/vBcubcwRwL2Bd/vXeAXgF8Fu3Jk1T4hs7tkD7AA+BRzky+8bvZ4fAnv5x/sXPot/Av4stAO+AeyDSyOzty8/EjjdP/4bhml4VoFLxZ9p1vc5ZOfutFu7zfO2Bw27I24OvEFV13GZXz8G3Aj4FfAZVT0XQNxZN4cDvwa+o6pn++vfgPvT3ihuCbwQQFW/KCJf9OU3wW2l/a8nSnviJreREJFL4/7AP+aLXofLSAwu0ebRgY0Al8b9Wd8ceLOqDoAfi8hHsm7fOMaeawDXAT7gy1dx+anATXInicj/4PKejcIf4TJhP94/3xs4DDcxvVhErg+sA1f39Z8FXiUiO4D/UdUvFPpuaFgo2sSye8I6hyLg4ujxOu47Umpfwi6G2617Z3VWkjrBHdh2/wnGkBF9hbq/UtX3JYUidxnT529K9ojIdYGzVPWmxrV3wU2cdwf+XkSOUlXrbBIB7qWqX8/6fhrwE9wRvyvA7wBU9ePizs+5C/A6EXmeqv7XmNfR0LAQNI1l98THgft6DeIg3B9hKV3414CriMjh/vl9R7S7ELhU9Pwc3Hnx4Laf4vEfAN156Uf78tOAm4nI1XzdJUXk6hSg7iyeC0Tk5r7oAVH1+4BH+lU+InJ1cUcGfBK4l9daLo/bArQwyp6vAweJyE19+Q4ROUpEVoBDVfUjuFNE9wdGaUTvA/4q0rBu4MsvDfzIs6kH4tgQInJl3CFyLwdeiTuzBmAtvL6Ghq2CNrHsnngbbsvmTODDOE3hx6Maq+pFwF8C7/Wi9k9wWkyOU4C/9WL0VXHH6D5SRD6F0ysCXgrs67fAnoCf1FT1ZzjN5g2+7jTgmhWv5yHAf3jx/qKo/BW4VO6fE+eC/DIcA3srLqV6KPs/6/WMskdVd+ImyueIyJnAF3AHbq0CrxeRL+GOdHiBn/gsPAOn1XzR2/YMX/4S4EEichpuGyywp1sBXxCRz+M0sn/35Sf6Ppp437Bl0NLmN1RBRPZV1V/7FfZ/AN9U1RcsyJaPAo9X1dM30Ud4PZfFTWw3K02uywTvfXeMqp63aFsadk80xtJQi7/wYv5ZuO2aly3QlvOB14gRIDkB3ulfzyeAZ2yHSUV8gCSOCQ0WbE7DbozGWBoaZggReQjwmKz4f1X1eKt9Q8N2QJtYGhoaGhqmirYV1tDQ0NAwVbSJpaGhoaFhqmgTS0NDQ0PDVNEmloaGhoaGqeL/A8vzoXtTNbdaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute array of distances from  blob, put them in result xarray DataArray\n",
    "\n",
    "dist = points.distance(blob.geometry).values.reshape(len(dsr.lat),len(dsr.lon))\n",
    "isin = points.within(blob.geometry).values.reshape(len(dsr.lat),len(dsr.lon))\n",
    "dist *= 2*(isin-0.5)  # signed distance from boundary, positive is interior \n",
    "\n",
    "result.values = dist\n",
    "result.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "7bdaed39-a138-483e-9174-67cf05f4dc3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1780155d0>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "327914a0-ca8b-46f3-95b8-71d0a5a3b118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        POINT (30.00000 -25.00000)\n",
       "1        POINT (30.62500 -25.00000)\n",
       "2        POINT (31.25000 -25.00000)\n",
       "3        POINT (31.87500 -25.00000)\n",
       "4        POINT (32.50000 -25.00000)\n",
       "                    ...            \n",
       "11408     POINT (97.50000 25.00000)\n",
       "11409     POINT (98.12500 25.00000)\n",
       "11410     POINT (98.75000 25.00000)\n",
       "11411     POINT (99.37500 25.00000)\n",
       "11412    POINT (100.00000 25.00000)\n",
       "Length: 11413, dtype: geometry"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "bfcabcce-3f53-498d-9e7d-3549a1cce8ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        POINT (30.00000 -25.00000)\n",
       "1        POINT (30.62500 -25.00000)\n",
       "2        POINT (31.25000 -25.00000)\n",
       "3        POINT (31.87500 -25.00000)\n",
       "4        POINT (32.50000 -25.00000)\n",
       "                    ...            \n",
       "11408     POINT (97.50000 25.00000)\n",
       "11409     POINT (98.12500 25.00000)\n",
       "11410     POINT (98.75000 25.00000)\n",
       "11411     POINT (99.37500 25.00000)\n",
       "11412    POINT (100.00000 25.00000)\n",
       "Length: 11413, dtype: geometry"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points.set_crs(epsg = \"4326\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "35adc8a4-5f44-4298-bf86-abac93dad0ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v9/8pr92c8s0m14426rc7jfm2c80000gn/T/ipykernel_96932/3631138782.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'distance' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  distall = points.distance(gdf_area.geometry[100])\n"
     ]
    }
   ],
   "source": [
    "distall = points.distance(gdf_area.geometry[100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "69846152-7a0a-4318-b6f0-dd9cff8b60ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        Series\n",
       "\u001b[0;31mString form:\u001b[0m\n",
       "0        22.949765\n",
       "           1        22.562058\n",
       "           2        22.163098\n",
       "           3        21.767574\n",
       "           4        21.382797\n",
       "           <...>  57.316684\n",
       "           11410    57.856646\n",
       "           11411    58.398304\n",
       "           11412    58.941612\n",
       "           Length: 11413, dtype: float64\n",
       "\u001b[0;31mLength:\u001b[0m      11413\n",
       "\u001b[0;31mFile:\u001b[0m        ~/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/pandas/core/series.py\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "One-dimensional ndarray with axis labels (including time series).\n",
       "\n",
       "Labels need not be unique but must be a hashable type. The object\n",
       "supports both integer- and label-based indexing and provides a host of\n",
       "methods for performing operations involving the index. Statistical\n",
       "methods from ndarray have been overridden to automatically exclude\n",
       "missing data (currently represented as NaN).\n",
       "\n",
       "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
       "associated index values-- they need not be the same length. The result\n",
       "index will be the sorted union of the two indexes.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "data : array-like, Iterable, dict, or scalar value\n",
       "    Contains data stored in Series. If data is a dict, argument order is\n",
       "    maintained.\n",
       "index : array-like or Index (1d)\n",
       "    Values must be hashable and have the same length as `data`.\n",
       "    Non-unique index values are allowed. Will default to\n",
       "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
       "    and index is None, then the keys in the data are used as the index. If the\n",
       "    index is not None, the resulting Series is reindexed with the index values.\n",
       "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
       "    Data type for the output Series. If not specified, this will be\n",
       "    inferred from `data`.\n",
       "    See the :ref:`user guide <basics.dtypes>` for more usages.\n",
       "name : str, optional\n",
       "    The name to give to the Series.\n",
       "copy : bool, default False\n",
       "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "Constructing Series from a dictionary with an Index specified\n",
       "\n",
       ">>> d = {'a': 1, 'b': 2, 'c': 3}\n",
       ">>> ser = pd.Series(data=d, index=['a', 'b', 'c'])\n",
       ">>> ser\n",
       "a   1\n",
       "b   2\n",
       "c   3\n",
       "dtype: int64\n",
       "\n",
       "The keys of the dictionary match with the Index values, hence the Index\n",
       "values have no effect.\n",
       "\n",
       ">>> d = {'a': 1, 'b': 2, 'c': 3}\n",
       ">>> ser = pd.Series(data=d, index=['x', 'y', 'z'])\n",
       ">>> ser\n",
       "x   NaN\n",
       "y   NaN\n",
       "z   NaN\n",
       "dtype: float64\n",
       "\n",
       "Note that the Index is first build with the keys from the dictionary.\n",
       "After this the Series is reindexed with the given Index values, hence we\n",
       "get all NaN as a result.\n",
       "\n",
       "Constructing Series from a list with `copy=False`.\n",
       "\n",
       ">>> r = [1, 2]\n",
       ">>> ser = pd.Series(r, copy=False)\n",
       ">>> ser.iloc[0] = 999\n",
       ">>> r\n",
       "[1, 2]\n",
       ">>> ser\n",
       "0    999\n",
       "1      2\n",
       "dtype: int64\n",
       "\n",
       "Due to input data type the Series has a `copy` of\n",
       "the original data even though `copy=False`, so\n",
       "the data is unchanged.\n",
       "\n",
       "Constructing Series from a 1d ndarray with `copy=False`.\n",
       "\n",
       ">>> r = np.array([1, 2])\n",
       ">>> ser = pd.Series(r, copy=False)\n",
       ">>> ser.iloc[0] = 999\n",
       ">>> r\n",
       "array([999,   2])\n",
       ">>> ser\n",
       "0    999\n",
       "1      2\n",
       "dtype: int64\n",
       "\n",
       "Due to input data type the Series has a `view` on\n",
       "the original data, so\n",
       "the data is changed as well.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distall?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dba77bd-d5f6-4452-872d-dd9ed372ae6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "222c772f-e808-4951-8ae7-3200b61c68f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b10a9af-dcee-47a6-b025-b9bfec656185",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c581ed88-6c7d-4e29-bb4f-b59430d8cd50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "82563549-959f-427f-bcee-d1fb951437e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x16bdee0b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lat = dsrblobuf.lat.data\n",
    "lon = dsrblobuf.lon.data\n",
    "\n",
    "distance = np.sqrt((lat - centroidlat)[:,None]**2 + ((lon - centroidlon)**2))\n",
    "plt.contour(lon, lat, distance); plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fdd8d842-dba9-4c44-acd1-585bd716521d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x16f060430>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle = np.arctan2((lat - centroidlat)[:,None], -(lon - centroidlon))\n",
    "plt.contourf(lon, lat, angle, 32); plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "242d8d39-e3f4-4380-aedf-87a3c32de034",
   "metadata": {},
   "outputs": [
    {
     "ename": "MissingDimensionsError",
     "evalue": "cannot set variable 'distance' with 2-dimensional data without explicit dimension names. Pass a tuple of (dims, data) instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMissingDimensionsError\u001b[0m                    Traceback (most recent call last)",
      "Input \u001b[0;32mIn [40]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdsrb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdistance\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/xarray/core/dataset.py:5273\u001b[0m, in \u001b[0;36mDataset.assign\u001b[0;34m(self, variables, **variables_kwargs)\u001b[0m\n\u001b[1;32m   5271\u001b[0m results \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39m_calc_assign_results(variables)\n\u001b[1;32m   5272\u001b[0m \u001b[38;5;66;03m# ... and then assign\u001b[39;00m\n\u001b[0;32m-> 5273\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/xarray/core/dataset.py:4253\u001b[0m, in \u001b[0;36mDataset.update\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   4217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate\u001b[39m(\u001b[38;5;28mself\u001b[39m, other: CoercibleMapping) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m   4218\u001b[0m     \u001b[38;5;124;03m\"\"\"Update this dataset's variables with those from another dataset.\u001b[39;00m\n\u001b[1;32m   4219\u001b[0m \n\u001b[1;32m   4220\u001b[0m \u001b[38;5;124;03m    Just like :py:meth:`dict.update` this is a in-place operation.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4251\u001b[0m \u001b[38;5;124;03m    Dataset.merge\u001b[39;00m\n\u001b[1;32m   4252\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4253\u001b[0m     merge_result \u001b[38;5;241m=\u001b[39m \u001b[43mdataset_update_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4254\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_replace(inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmerge_result\u001b[38;5;241m.\u001b[39m_asdict())\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/xarray/core/merge.py:988\u001b[0m, in \u001b[0;36mdataset_update_method\u001b[0;34m(dataset, other)\u001b[0m\n\u001b[1;32m    985\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    986\u001b[0m         indexes[key] \u001b[38;5;241m=\u001b[39m index\n\u001b[0;32m--> 988\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmerge_core\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    989\u001b[0m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    990\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpriority_arg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    991\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindexes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m    992\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcombine_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moverride\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    993\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/xarray/core/merge.py:632\u001b[0m, in \u001b[0;36mmerge_core\u001b[0;34m(objects, compat, join, combine_attrs, priority_arg, explicit_coords, indexes, fill_value)\u001b[0m\n\u001b[1;32m    628\u001b[0m coerced \u001b[38;5;241m=\u001b[39m coerce_pandas_values(objects)\n\u001b[1;32m    629\u001b[0m aligned \u001b[38;5;241m=\u001b[39m deep_align(\n\u001b[1;32m    630\u001b[0m     coerced, join\u001b[38;5;241m=\u001b[39mjoin, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, indexes\u001b[38;5;241m=\u001b[39mindexes, fill_value\u001b[38;5;241m=\u001b[39mfill_value\n\u001b[1;32m    631\u001b[0m )\n\u001b[0;32m--> 632\u001b[0m collected \u001b[38;5;241m=\u001b[39m \u001b[43mcollect_variables_and_indexes\u001b[49m\u001b[43m(\u001b[49m\u001b[43maligned\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    634\u001b[0m prioritized \u001b[38;5;241m=\u001b[39m _get_priority_vars_and_indexes(aligned, priority_arg, compat\u001b[38;5;241m=\u001b[39mcompat)\n\u001b[1;32m    635\u001b[0m variables, out_indexes \u001b[38;5;241m=\u001b[39m merge_collected(\n\u001b[1;32m    636\u001b[0m     collected, prioritized, compat\u001b[38;5;241m=\u001b[39mcompat, combine_attrs\u001b[38;5;241m=\u001b[39mcombine_attrs\n\u001b[1;32m    637\u001b[0m )\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/xarray/core/merge.py:291\u001b[0m, in \u001b[0;36mcollect_variables_and_indexes\u001b[0;34m(list_of_mappings)\u001b[0m\n\u001b[1;32m    288\u001b[0m     indexes\u001b[38;5;241m.\u001b[39mpop(name, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    289\u001b[0m     append_all(coords, indexes)\n\u001b[0;32m--> 291\u001b[0m variable \u001b[38;5;241m=\u001b[39m \u001b[43mas_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m variable\u001b[38;5;241m.\u001b[39mdims \u001b[38;5;241m==\u001b[39m (name,):\n\u001b[1;32m    294\u001b[0m     idx_variable \u001b[38;5;241m=\u001b[39m variable\u001b[38;5;241m.\u001b[39mto_index_variable()\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/geopy/lib/python3.10/site-packages/xarray/core/variable.py:134\u001b[0m, in \u001b[0;36mas_variable\u001b[0;34m(obj, name)\u001b[0m\n\u001b[1;32m    132\u001b[0m     data \u001b[38;5;241m=\u001b[39m as_compatible_data(obj)\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 134\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m MissingDimensionsError(\n\u001b[1;32m    135\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot set variable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m with \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdata\u001b[38;5;241m.\u001b[39mndim\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m-dimensional data \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    136\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwithout explicit dimension names. Pass a tuple of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    137\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(dims, data) instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    138\u001b[0m         )\n\u001b[1;32m    139\u001b[0m     obj \u001b[38;5;241m=\u001b[39m Variable(name, data, fastpath\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mMissingDimensionsError\u001b[0m: cannot set variable 'distance' with 2-dimensional data without explicit dimension names. Pass a tuple of (dims, data) instead."
     ]
    }
   ],
   "source": []
  },
  {
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   "id": "0ff11e9c-4e51-4d47-af7c-9c9a94fc2182",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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